diff --git a/docker/serviceConfigs/ontop/mapping.obda b/docker/serviceConfigs/ontop/mapping.obda
index 77ab37ad4..809daba90 100644
--- a/docker/serviceConfigs/ontop/mapping.obda
+++ b/docker/serviceConfigs/ontop/mapping.obda
@@ -8,7 +8,7 @@ foaf:        http://xmlns.com/foaf/0.1/
 obda:        https://w3id.org/obda/vocabulary#
 rdfs:        http://www.w3.org/2000/01/rdf-schema#
 oeo:         https://openenergyplatform.org/ontology/oeo/
-oekg:        http://openenergy-platform.org/ontology/oeo/oekg/
+oekg:        https://openenergyplatform.org/ontology/oeo/oekg/
 llc:         https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/
 
 [MappingDeclaration] @collection [[
@@ -18,183 +18,179 @@ target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} a oeo:IAO
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1"
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_AT
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Austria>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Austria .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'AT'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_BE
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Belgium>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Belgium .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'BE'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_BG
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Bulgaria>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Bulgaria .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'BG'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_CH
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Switzerland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Switzerland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'CH'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_CY
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Cyprus>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Cyprus .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'CY'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_CZ
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Czechia>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Czechia .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'CZ'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_DE
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Germany>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Germany .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'DE'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_DK
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Denmark>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Denmark .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'DK'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_EE
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Estonia>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Estonia .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'EE'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_EL
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Greece>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Greece .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'EL'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_ES
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Spain>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Spain .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'ES'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_FI
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Finland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Finland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'FI'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_FR
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/France>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:France .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'FR'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_GB
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/UnitedKingdomOfGreatBritainAndNorthernIreland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:UnitedKingdomOfGreatBritainAndNorthernIreland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'GB'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_GR
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Greece>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Greece .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'GR'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_HR
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Croatia>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Croatia .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'HR'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_HU
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Hungary>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Hungary .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'HU'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_IE
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Ireland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Ireland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'IE'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_IS
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Iceland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Iceland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'IS'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_IT
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Italy>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Italy .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'IT'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_LT
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Lithuania>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Lithuania .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'LT'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_LU
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Luxembourg>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Luxembourg .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'LU'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_LV
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Latvia>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Latvia .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'LV'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_MT
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Malta>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Malta .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'MT'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_NL
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Netherlands>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Netherlands .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'NL'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_NO
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Norway>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Norway .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'NO'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_PL
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Poland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Poland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'PL'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_PT
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Portugal>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Portugal .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'PT'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_RO
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Romania>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Romania .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'RO'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_SE
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Sweden>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Sweden .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'SE'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_SI
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Slovenia>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Slovenia .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'SI'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_SK
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/Slovakia>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:Slovakia .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'SK'
 
 mappingId       eu_leg_data_2021_rep_table_1_spatial_region_UK
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 <https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/UnitedKingdomOfGreatBritainAndNorthernIreland>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020221 llc:UnitedKingdomOfGreatBritainAndNorthernIreland .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "country_code" = 'UK'
 
-mappingId       eu_leg_data_2021_rep_table_1_crf_based_sector_division
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division "{category}" .
-source          SELECT "id", "category" FROM "data"."eu_leg_data_2021_rep_table_1"
-
 mappingId       eu_leg_data_2021_rep_table_1_scenario_year
 target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020224 "{year}" .
 source          SELECT "id", "year" FROM "data"."eu_leg_data_2021_rep_table_1"
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_CH4
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000025>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000025 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'CH4'
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_CO2
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000006>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000006 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'CO2'
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_N2O
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000027>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000027 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'N2O'
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_NF3
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000026>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000026 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'NF3'
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_HFC
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000219>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000219 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'HFC'
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_PFC
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000322>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000322 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'PFC'
 
 mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_SF6
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 <http://openenergy-platform.org/ontology/oeo/OEO_00000038>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00010121 oeo:OEO_00000038 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "gas" = 'SF6'
 
 mappingId       eu_leg_data_2021_rep_table_1_scenario_WEM
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020226 <http://openenergy-platform.org/ontology/oeo/OEO_00020311>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020226 oeo:OEO_00020311 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "scenario" = 'WEM'
 
 mappingId       eu_leg_data_2021_rep_table_1_scenario_WAM
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020226 <http://openenergy-platform.org/ontology/oeo/OEO_00020312>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020226 oeo:OEO_00020312 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "scenario" = 'WAM'
 
 mappingId       eu_leg_data_2021_rep_table_1_scenario_WOM
-target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020226 <http://openenergy-platform.org/ontology/oeo/OEO_00020310>.
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00020226 oeo:OEO_00020310 .
 source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "scenario" = 'WOM'
 
 mappingId       eu_leg_data_2021_rep_table_1_unit_of_measurement
@@ -205,4 +201,260 @@ mappingId       eu_leg_data_2021_rep_table_1_greenhouse_gas_emission_value
 target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:OEO_00140178 "{value}" .
 source          SELECT "id", "value" FROM "data"."eu_leg_data_2021_rep_table_1"
 
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010038
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010038 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1 Energy'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010039
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010039 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A Fuel combustion'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010040
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010040 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.1 Energy industries'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010158
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010158 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.1.a Public electricity and heat production'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010159
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010159 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.1.b Petroleum refining'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010160
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010160 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.1.c Manufacture of solid fuels and other energy industries'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010041
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010041 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.2 Manufacturing industries and construction'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010042
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010042 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.3 Transport'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010059
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010059 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.3.a Domestic aviation'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010060
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010060 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.3.b Road transportation'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010061
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010061 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.3.c Railways'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010062
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010062 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.3.d Domestic navigation'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010063
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010063 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.3.e Other transportation'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010043
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010043 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.4 Other sectors'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010052
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010052 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.4.a Commercial/Institutional'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010053
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010053 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.4.b Residential'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010054
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010054 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.4.c Agriculture/Forestry/Fishing'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010044
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010044 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.A.5 Other'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010057
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010057 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.B Fugitive emissions from fuels'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010161
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010161 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.B.1 Solid fuels'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010162
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010162 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.B.2 Oil and natural gas and other emissions from energy production'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010058
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010058 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '1.C CO2 transport and storage'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010046
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010046 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2 Industrial processes'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010164
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010164 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.A Mineral Industry'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010165
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010165 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.A.1 Cement production'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010166
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010166 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.B Chemical industry'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010167
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010167 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.C Metal industry'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010168
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010168 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.C.1 Iron and steel production'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010169
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010169 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.D Non-energy products from fuels and solvent use'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010170
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010170 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.E Electronics industry'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010171
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010171 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.F Product uses as substitutes for ODS'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010172
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010172 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.G Other product manufacture and use'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010173
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010173 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '2.H Other'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010047
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010047 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3 Agriculture'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010179
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010179 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.A Enteric fermentation'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010180
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010180 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.B Manure management'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010181
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010181 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.C Rice cultivation'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010182
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010182 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.D Agricultural soils'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010183
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010183 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.E Prescribed burning of savannahs'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010184
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010184 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.F Field burning of agricultural residues'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010185
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010185 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.G Liming'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010186
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010186 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.H Urea application'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010187
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010187 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.I Other carbon-containing fertilizers'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010188
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010188 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '3.J Other'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010048
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010048 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '4 Land Use, Land-Use Change and Forestry'
+
+mappingId       eu_leg_data_2021_rep_table_1_category_OEO_00010189
+target          oekg:data-descriptor/eu_leg_data_2021_rep_table_1/{id} oeo:has_sector_division oeo:OEO_00010189 .
+source          SELECT "id" FROM "data"."eu_leg_data_2021_rep_table_1" WHERE "category" = '4.A Forest land'
+
+mappingId       ariadne2_data_with_labels_TargetClass
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} a oeo:IAO_0000027 ; oeo:OEO_00000504 "ariadne2_data_with_labels"^^xsd:string .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels"
+
+mappingId       ariadne2_data_with_labels_scenario_year
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:OEO_00020224 "{scenario_year}" .
+source          SELECT "id", "scenario_year" FROM "data"."ariadne2_data_with_labels"
+
+mappingId       ariadne2_data_with_labels_quantity_value
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:OEO_00140178 "{value}" .
+source          SELECT "id", "value" FROM "data"."ariadne2_data_with_labels"
+
+mappingId       ariadne2_data_with_labels_unit
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:OEO_00040010 "{unit}" .
+source          SELECT "id", "unit" FROM "data"."ariadne2_data_with_labels"
+
+mappingId       ariadne2_data_with_labels_scenario
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oekg:scenario "{scenario}" .
+source          SELECT "id", "scenario" FROM "data"."ariadne2_data_with_labels"
+
+mappingId       ariadne2_data_with_labels_quantity_kind
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oekg:quantity_kind "{quantity_kind}" .
+source          SELECT "id", split_part("iamc_full_string", ' | ', 1) AS quantity_kind FROM "data"."ariadne2_data_with_labels"
+
+mappingId       ariadne2_data_with_labels_spatial_region_DEU
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:OEO_00020221 llc:Germany .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE "region" = 'DEU'
+
+mappingId       ariadne2_data_with_labels_sector_transportation
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00000422 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | transportation | %'
+
+mappingId       ariadne2_data_with_labels_transport_mode_bus
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010277 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | bus | %'
+
+mappingId       ariadne2_data_with_labels_transport_mode_rail
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010280 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | rail | %'
+
+mappingId       ariadne2_data_with_labels_transport_mode_truck
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010278 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | truck | %'
+
+mappingId       ariadne2_data_with_labels_transport_mode_domestic_aviation
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010059 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | domestic aviation | %'
+
+mappingId       ariadne2_data_with_labels_transport_mode_domestic_navigation
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010062 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | domestic navigation | %'
+
+mappingId       ariadne2_data_with_labels_technology_bev
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010024 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | bev | %'
+
+mappingId       ariadne2_data_with_labels_technology_fcev
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010025 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | fcev | %'
+
+mappingId       ariadne2_data_with_labels_technology_ice
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00000240 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | ice | %'
+
+mappingId       ariadne2_data_with_labels_technology_phev
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00010030 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | phev | %'
+
+mappingId       ariadne2_data_with_labels_technology_overhead_line
+target          oekg:data-descriptor/ariadne2_data_with_labels/{id} oeo:IAO_0000136 oeo:OEO_00000047 .
+source          SELECT "id" FROM "data"."ariadne2_data_with_labels" WHERE (' | ' || "iamc_full_string" || ' | ') LIKE '% | overhead line | %'
+
 ]]
diff --git a/factsheet/frontend/src/components/comparison/RegistryComparison.jsx b/factsheet/frontend/src/components/comparison/RegistryComparison.jsx
new file mode 100644
index 000000000..ab26c8aca
--- /dev/null
+++ b/factsheet/frontend/src/components/comparison/RegistryComparison.jsx
@@ -0,0 +1,519 @@
+// SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+//
+// SPDX-License-Identifier: AGPL-3.0-or-later
+
+// Registry-driven quantitative comparison (beta). Works for ANY annotated table:
+// dimensions, predicates, value IRIs and labels come from /oekg/registry/, with
+// ontology labels/definitions loaded from the TIB Terminology Service.
+//
+// UX:
+//   * Only dimensions/presets the table actually populates are shown (discovery).
+//   * Presets give one-click → result; a plain-language customiser with ontology
+//     labels (not variable keys).
+//   * Unit selector — values are only summed within one unit (mixing is wrong).
+//   * "How it works" decomposition reveal tucked into an accordion.
+
+import React, { useEffect, useMemo, useState } from "react";
+import ReactECharts from "echarts-for-react";
+import axios from "axios";
+import Box from "@mui/material/Box";
+import Grid from "@mui/material/Grid";
+import Stack from "@mui/material/Stack";
+import Card from "@mui/material/Card";
+import CardContent from "@mui/material/CardContent";
+import Typography from "@mui/material/Typography";
+import Chip from "@mui/material/Chip";
+import Tooltip from "@mui/material/Tooltip";
+import Link from "@mui/material/Link";
+import Button from "@mui/material/Button";
+import TextField from "@mui/material/TextField";
+import MenuItem from "@mui/material/MenuItem";
+import Alert from "@mui/material/Alert";
+import LinearProgress from "@mui/material/LinearProgress";
+import Accordion from "@mui/material/Accordion";
+import AccordionSummary from "@mui/material/AccordionSummary";
+import AccordionDetails from "@mui/material/AccordionDetails";
+import ExpandMoreIcon from "@mui/icons-material/ExpandMore";
+
+import conf from "../../conf.json";
+import CSRFToken from "../csrfToken.js";
+import useRegistry from "./useRegistry.js";
+import {
+  buildComparisonQuery, labelForIri, expandCurie,
+  dimensionAskQuery, valueFrequencyQuery,
+} from "./registryQuery.js";
+import { resolveTerms } from "./tibTerms.js";
+
+const PALETTE = [
+  "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd",
+  "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf",
+];
+const DELIM = " | ";
+const ROWS_SCHEMA = "model_draft";
+
+const titleCase = (k) => k.replace(/_/g, " ").replace(/\b\w/g, (c) => c.toUpperCase());
+const shorten = (s) => (s && s.startsWith("http") ? s.split("/").pop() : s);
+const cellValue = (registry, dim, row) => {
+  const b = row[dim.key];
+  if (!b) return null;
+  return dim.object_kind === "iri" ? labelForIri(registry, dim, b.value) : b.value;
+};
+
+function TermChip({ info, fallbackLabel, fullIri, color }) {
+  const label = info?.label || shorten(fallbackLabel);
+  const desc = info?.description || "Loading definition…";
+  return (
+    <Tooltip arrow placement="top" title={<Typography variant="body2" sx={{ p: 0.5 }}>{desc}</Typography>}>
+      <Chip label={label} size="small"
+        component={fullIri ? Link : "div"} href={fullIri || undefined} target="_blank" clickable={!!fullIri}
+        variant="outlined" sx={{ cursor: fullIri ? "pointer" : "help", fontWeight: 600, borderColor: color, color }} />
+    </Tooltip>
+  );
+}
+
+export default function RegistryComparison() {
+  const { registry, loading: registryLoading, error: registryError } = useRegistry();
+
+  const [table, setTable] = useState("ariadne2_data_with_labels");
+  const [xKey, setXKey] = useState("scenario_year");
+  const [stackKey, setStackKey] = useState("technology");
+  const [unitOptions, setUnitOptions] = useState([]);
+  const [unit, setUnit] = useState("");
+  const [quantityOptions, setQuantityOptions] = useState([]); // [{value,count}] most common first
+  const [primaryQuantity, setPrimaryQuantity] = useState(""); // "" = all quantities
+  const [chartType, setChartType] = useState("stacked"); // stacked | grouped | line
+  const [availableKeys, setAvailableKeys] = useState(null); // Set | null(=show all)
+  const [discovering, setDiscovering] = useState(false);
+  const [rawString, setRawString] = useState(null);
+  const [segments, setSegments] = useState([]);
+  const [rows, setRows] = useState(null);
+  const [terms, setTerms] = useState({});
+  const [dimTerms, setDimTerms] = useState({});
+  const [running, setRunning] = useState(false);
+  const [err, setErr] = useState(null);
+  const [lastQuery, setLastQuery] = useState("");
+
+  const allAxisDims = useMemo(
+    () => (registry?.dimensions || []).filter((d) => !["quantity_value", "unit"].includes(d.key)),
+    [registry]
+  );
+  const axisDims = useMemo(
+    () => (availableKeys ? allAxisDims.filter((d) => availableKeys.has(d.key)) : allAxisDims),
+    [allAxisDims, availableKeys]
+  );
+  const dimLabel = (d) => (d?.concept && dimTerms[d.concept]?.label) || titleCase(d?.key || "");
+
+  // short inline hint: how rows qualify for this dimension
+  const dimSourceShort = (d) =>
+    !d ? "" :
+    d.value_space === "iamc_tokens" ? "matched from variable-string tokens" :
+    d.object_kind === "literal" ? "raw value of the column" :
+    "controlled value → ontology IRI";
+  // full hover: how rows qualify + which predicate + ontology definition
+  const dimTooltip = (d) => {
+    if (!d) return "";
+    const how =
+      d.value_space === "iamc_tokens"
+        ? `Qualifies rows whose variable string contains a ${titleCase(d.key)} token (e.g. ${(d.values || []).slice(0, 4).map((v) => v.code).join(", ")}).`
+        : d.object_kind === "literal"
+        ? `Uses the raw value of the “${titleCase(d.key)}” column.`
+        : `Qualifies rows whose value maps to an ontology IRI (controlled vocabulary).`;
+    const def = d.concept && dimTerms[d.concept]?.description;
+    return `${how} — predicate ${d.predicate}.${def ? " · " + def : ""}`;
+  };
+
+  const tokenIndex = useMemo(() => {
+    const idx = {};
+    for (const d of registry?.dimensions || []) {
+      if (d.value_space !== "iamc_tokens") continue;
+      for (const v of d.values || []) idx[v.code.toLowerCase()] = { ...v, dimension: d.key };
+    }
+    return idx;
+  }, [registry]);
+
+  const postSparql = async (query) => {
+    const res = await axios.post(conf.obdi, query, {
+      headers: {
+        "X-CSRFToken": CSRFToken(),
+        Accept: "application/sparql-results+json",
+        "Content-Type": "application/sparql-query",
+      },
+    });
+    return res.data;
+  };
+
+  const timeDim = useMemo(
+    () => axisDims.find((d) => d.key === "scenario_year") ||
+          axisDims.find((d) => (d.datatype || "").includes("gYear")) || null,
+    [axisDims]
+  );
+  // dimensions offered as breakdowns/axes (quantity is the primary filter, not a breakdown)
+  const breakdownDims = useMemo(
+    () => axisDims.filter((d) => !(primaryQuantity && d.key === "quantity_kind")),
+    [axisDims, primaryQuantity]
+  );
+  const presets = useMemo(() => {
+    if (!timeDim) return [];
+    return breakdownDims
+      .filter((d) => d.key !== timeDim.key &&
+        (d.object_kind !== "iri" || (d.values || []).length > 0)) // literal dims always groupable
+      .map((d) => ({ name: `${titleCase(d.key)} over ${titleCase(timeDim.key)}`, x: timeDim.key, stack: d.key }));
+  }, [breakdownDims, timeDim]);
+
+  // dimension concept labels (for the controls)
+  useEffect(() => {
+    if (!registry) return;
+    let active = true;
+    resolveTerms((registry.dimensions || []).map((d) => d.concept).filter(Boolean))
+      .then((m) => active && setDimTerms(m));
+    return () => { active = false; };
+  }, [registry]);
+
+  // DISCOVERY: which dimensions/units does THIS table populate?
+  useEffect(() => {
+    if (!registry) return;
+    let active = true;
+    setDiscovering(true); setAvailableKeys(null); setRows(null);
+    (async () => {
+      const t = table.trim();
+      // availability via ASK per dimension
+      let avail = null;
+      try {
+        const res = await Promise.all(allAxisDims.map(async (dm) => {
+          try { const r = await postSparql(dimensionAskQuery({ registry, table: t, dim: dm })); return [dm.key, !!r.boolean]; }
+          catch { return [dm.key, true]; }
+        }));
+        avail = new Set(res.filter(([, ok]) => ok).map(([k]) => k));
+      } catch (e) { avail = null; }
+      // primary quantity options (the first IAMC segment), most common first
+      let quants = [];
+      try {
+        const qd = allAxisDims.find((d) => d.key === "quantity_kind");
+        if (qd) {
+          const r = await postSparql(valueFrequencyQuery({ registry, table: t, dim: qd }));
+          quants = (r.results?.bindings || []).map((b) => ({ value: b.v.value, count: +(b.c?.value || 0) }));
+        }
+      } catch (e) { /* ignore */ }
+      if (!active) return;
+      setAvailableKeys(avail);
+      setQuantityOptions(quants);
+      setPrimaryQuantity((cur) => (quants.find((q) => q.value === cur) ? cur : (quants[0]?.value || "")));
+      if (avail) {
+        // quantity_kind is the PRIMARY filter, not a breakdown
+        const keys = [...avail].filter((k) => k !== "quantity_kind");
+        setXKey((cur) => (avail.has(cur) ? cur : (avail.has("scenario_year") ? "scenario_year" : keys[0])) || cur);
+        setStackKey((cur) => (avail.has(cur) && cur !== "quantity_kind" ? cur : (keys.find((k) => k !== "scenario_year") || keys[0])) || cur);
+      }
+      setDiscovering(false);
+    })();
+    return () => { active = false; };
+  }, [registry, table, allAxisDims]);
+
+  // UNITS — follow the chosen quantity (the unit is determined by the quantity)
+  useEffect(() => {
+    if (!registry) return;
+    let active = true;
+    (async () => {
+      const unitDim = (registry.dimensions || []).find((d) => d.key === "unit");
+      const qkDim = allAxisDims.find((d) => d.key === "quantity_kind");
+      if (!unitDim) return;
+      try {
+        const d = await postSparql(valueFrequencyQuery({
+          registry, table: table.trim(), dim: unitDim,
+          scopeDim: primaryQuantity ? qkDim : null, scopeValue: primaryQuantity || null,
+        }));
+        const us = (d.results?.bindings || []).map((b) => b.v.value);
+        if (!active) return;
+        setUnitOptions(us);
+        setUnit((cur) => (us.includes(cur) ? cur : (us[0] || "")));
+      } catch (e) { if (active) { setUnitOptions([]); setUnit(""); } }
+    })();
+    return () => { active = false; };
+  }, [registry, table, primaryQuantity, allAxisDims]);
+
+  // sample row + IAMC decomposition (only if present)
+  useEffect(() => {
+    if (!registry) return;
+    let active = true;
+    (async () => {
+      try {
+        const res = await axios.get(`/api/v0/schema/${ROWS_SCHEMA}/tables/${table.trim()}/rows/?limit=80`);
+        const data = Array.isArray(res.data) ? res.data : [];
+        const withStr = data.filter((r) => r.iamc_full_string);
+        if (!withStr.length) { if (active) { setRawString(null); setSegments([]); } return; }
+        const best = withStr.sort((a, b) => b.iamc_full_string.split("|").length - a.iamc_full_string.split("|").length)[0];
+        const raw = best.iamc_full_string;
+        const segs = raw.split(DELIM).map((s) => s.trim()).filter(Boolean)
+          .map((seg) => ({ seg, match: tokenIndex[seg.toLowerCase()] || null }));
+        if (!active) return;
+        setRawString(raw); setSegments(segs);
+        const map = await resolveTerms(segs.map((s) => s.match?.iri).filter(Boolean));
+        if (active) setTerms((p) => ({ ...p, ...map }));
+      } catch (e) { if (active) { setRawString(null); setSegments([]); } }
+    })();
+    return () => { active = false; };
+  }, [registry, table, tokenIndex]);
+
+  const run = async (xk = xKey, sk = stackKey, u = unit, q = primaryQuantity) => {
+    setXKey(xk); setStackKey(sk);
+    setRunning(true); setErr(null); setRows(null);
+    try {
+      const filters = {};
+      const dimset = new Set([xk, sk]);
+      if (u) { dimset.add("unit"); filters.unit = [u]; }
+      // scope to the primary quantity (unless it IS an axis here)
+      if (q && xk !== "quantity_kind" && sk !== "quantity_kind") {
+        dimset.add("quantity_kind"); filters.quantity_kind = [q];
+      }
+      const query = buildComparisonQuery({ registry, tables: [table.trim()], dims: [...dimset], filters });
+      setLastQuery(query);
+      const data = await postSparql(query);
+      const bindings = data?.results?.bindings || [];
+      setRows(bindings);
+      const stackDim = allAxisDims.find((d) => d.key === sk);
+      if (stackDim?.object_kind === "iri") {
+        const map = await resolveTerms(bindings.map((r) => r[sk]?.value).filter(Boolean));
+        setTerms((p) => ({ ...p, ...map }));
+      }
+    } catch (e) { setErr(e?.message || "Query failed"); } finally { setRunning(false); }
+  };
+
+  // preset: pick the breakdown AND the most relevant unit (for the current
+  // primary quantity), then run scoped to that quantity.
+  const runPreset = async (p) => {
+    setXKey(p.x); setStackKey(p.stack);
+    const unitDim = (registry.dimensions || []).find((d) => d.key === "unit");
+    const qkDim = allAxisDims.find((d) => d.key === "quantity_kind");
+    let u = unit;
+    try {
+      const d = await postSparql(valueFrequencyQuery({
+        registry, table: table.trim(), dim: unitDim,
+        scopeDim: primaryQuantity ? qkDim : null, scopeValue: primaryQuantity || null,
+      }));
+      const us = (d.results?.bindings || []).map((b) => b.v.value);
+      setUnitOptions(us);
+      u = us[0] || "";
+      setUnit(u);
+    } catch (e) { /* keep current unit */ }
+    run(p.x, p.stack, u, primaryQuantity);
+  };
+
+  const seriesLabel = (fullIri, fallback) => {
+    const lbl = (fullIri && terms[fullIri]?.label) || fallback;
+    return shorten(lbl);
+  };
+
+  const { option, stackValues } = useMemo(() => {
+    if (!rows || !registry) return { option: null, stackValues: [] };
+    const xDim = allAxisDims.find((d) => d.key === xKey);
+    const stackDim = allAxisDims.find((d) => d.key === stackKey);
+    if (!xDim || !stackDim) return { option: null, stackValues: [] };
+    const xVals = [...new Set(rows.map((r) => cellValue(registry, xDim, r)).filter((v) => v != null))].sort();
+    const stacks = []; const matrix = {};
+    for (const r of rows) {
+      const x = cellValue(registry, xDim, r);
+      const sFull = stackDim.object_kind === "iri" ? r[stackDim.key]?.value : null;
+      const sLabel = seriesLabel(sFull, cellValue(registry, stackDim, r));
+      const val = parseFloat(r.value?.value);
+      if (x == null || sLabel == null || Number.isNaN(val)) continue;
+      if (!stacks.find((s) => s.label === sLabel)) stacks.push({ label: sLabel, fullIri: sFull });
+      matrix[sLabel] = matrix[sLabel] || {}; matrix[sLabel][x] = (matrix[sLabel][x] || 0) + val;
+    }
+    const isLine = chartType === "line";
+    const stackId = chartType === "stacked" ? "total" : undefined;
+    const opt = {
+      tooltip: { trigger: "axis", axisPointer: { type: isLine ? "line" : "shadow" } },
+      legend: { type: "scroll", top: 0 },
+      grid: { left: 80, right: 20, bottom: 40, top: 40 },
+      xAxis: { type: "category", data: xVals, name: titleCase(xDim.key) },
+      yAxis: { type: "value", name: unit || "value" },
+      series: stacks.map((s, i) => ({
+        name: s.label, type: isLine ? "line" : "bar",
+        ...(stackId ? { stack: stackId } : {}),
+        emphasis: { focus: "series" }, smooth: isLine,
+        itemStyle: { color: PALETTE[i % PALETTE.length] }, data: xVals.map((x) => matrix[s.label]?.[x] ?? 0),
+      })),
+    };
+    return { option: opt, stackValues: stacks };
+  }, [rows, registry, allAxisDims, xKey, stackKey, unit, terms, chartType]);
+
+  if (registryLoading) return <LinearProgress />;
+  if (registryError) return <Alert severity="error">Could not load the registry from {conf.dimensionRegistry}.</Alert>;
+
+  const xDimObj = allAxisDims.find((d) => d.key === xKey);
+  const stackDimObj = allAxisDims.find((d) => d.key === stackKey);
+
+  return (
+    <Box sx={{ px: 2 }}>
+      {/* PRIMARY VARIABLE — the top-level measured quantity; scopes everything
+          below. Only shown when the dataset actually has a quantity dimension
+          (so single-variable / typed tables aren't cluttered with it). */}
+      {quantityOptions.length > 0 && (
+      <Card variant="outlined" sx={{ mb: 2, borderColor: "primary.main" }}>
+        <CardContent sx={{ py: 1.5 }}>
+          <Grid container spacing={2} alignItems="center">
+            <Grid item xs={12} md={6}>
+              <TextField select label="Primary variable (what to measure)" value={primaryQuantity} fullWidth size="small"
+                helperText="the measured quantity — scopes the unit, options & chart"
+                onChange={(e) => {
+                  const v = e.target.value;
+                  setPrimaryQuantity(v);
+                  if (v && stackKey === "quantity_kind") {
+                    const alt = axisDims.find((d) => d.key !== "quantity_kind" && d.key !== xKey);
+                    if (alt) setStackKey(alt.key);
+                  }
+                }}>
+                <MenuItem value="">(all variables — mixed)</MenuItem>
+                {quantityOptions.map((q) => (
+                  <MenuItem key={q.value} value={q.value}>{titleCase(q.value)}{q.count ? ` (${q.count})` : ""}</MenuItem>
+                ))}
+              </TextField>
+            </Grid>
+            <Grid item xs={12} md={6}>
+              <Typography variant="body2" color="text.secondary">
+                {primaryQuantity
+                  ? <>Scoped to <b>{titleCase(primaryQuantity)}</b> — pick a unit + a breakdown below.</>
+                  : <>All variables (mixed units) — pick one to focus the analysis and shrink the options.</>}
+              </Typography>
+            </Grid>
+          </Grid>
+        </CardContent>
+      </Card>
+      )}
+
+      {/* PRESETS */}
+      <Typography variant="subtitle2" color="text.secondary" gutterBottom>Quick comparisons (one click):</Typography>
+      <Stack direction="row" spacing={1} useFlexGap flexWrap="wrap" sx={{ mb: 2 }}>
+        {discovering && <Chip label="finding available comparisons…" size="small" />}
+        {!discovering && presets.length === 0 && <Typography variant="body2" color="text.secondary">No presets for this table.</Typography>}
+        {presets.map((p) => (
+          <Button key={p.name} size="small" variant="outlined" disabled={running} onClick={() => runPreset(p)}>{p.name}</Button>
+        ))}
+      </Stack>
+
+      {/* CUSTOMISER */}
+      <Card variant="outlined" sx={{ mb: 2 }}>
+        <CardContent>
+          <Typography variant="body1" sx={{ mb: 1.5 }}>
+            Show the total <b>value</b>{unit ? <> in <b>{unit}</b></> : null} across{" "}
+            <Tooltip title={dimTooltip(xDimObj)}><b style={{ borderBottom: "1px dotted" }}>{dimLabel(xDimObj)}</b></Tooltip>
+            , grouped by{" "}
+            <Tooltip title={dimTooltip(stackDimObj)}><b style={{ borderBottom: "1px dotted" }}>{dimLabel(stackDimObj)}</b></Tooltip>.
+          </Typography>
+          <Grid container spacing={2} alignItems="center">
+            <Grid item xs={12} md={3}>
+              <TextField label="Dataset (table)" value={table} onChange={(e) => setTable(e.target.value)} fullWidth size="small" helperText="one dataset at a time" />
+            </Grid>
+            <Grid item xs={6} md={3}>
+              <TextField select label="Across (x-axis)" value={xKey} fullWidth size="small"
+                helperText={dimSourceShort(xDimObj) || "usually time/years"} onChange={(e) => setXKey(e.target.value)}>
+                {breakdownDims.map((d) => <MenuItem key={d.key} value={d.key}>{dimLabel(d)}</MenuItem>)}
+              </TextField>
+            </Grid>
+            <Grid item xs={6} md={3}>
+              <TextField select label="Grouped by (bars)" value={stackKey} fullWidth size="small"
+                helperText={dimSourceShort(stackDimObj) || "dimension to break down"} onChange={(e) => setStackKey(e.target.value)}>
+                {breakdownDims.map((d) => <MenuItem key={d.key} value={d.key}>{dimLabel(d)}</MenuItem>)}
+              </TextField>
+            </Grid>
+            <Grid item xs={6} md={2}>
+              <TextField select label="Unit" value={unit} fullWidth size="small" disabled={!unitOptions.length}
+                helperText={unitOptions.length > 1 ? `${unitOptions.length} units — pick one` : "data unit"}
+                onChange={(e) => setUnit(e.target.value)}>
+                {unitOptions.map((u) => <MenuItem key={u} value={u}>{u}</MenuItem>)}
+              </TextField>
+            </Grid>
+            <Grid item xs={6} md={1}>
+              <Button variant="contained" onClick={() => run()} disabled={running} fullWidth>{running ? "…" : "Go"}</Button>
+            </Grid>
+          </Grid>
+          {unitOptions.length > 1 && (
+            <Typography variant="caption" color="text.secondary">
+              Only values in <b>{unit}</b> are summed — mixing units would be meaningless.
+            </Typography>
+          )}
+        </CardContent>
+      </Card>
+
+      {running && <LinearProgress sx={{ mb: 2 }} />}
+      {err && <Alert severity="error" sx={{ mb: 2 }}>{err}</Alert>}
+      {rows && rows.length === 0 && (
+        <Alert severity="warning">No data for this combination{unit ? <> in <b>{unit}</b></> : null}. Try a preset or another unit.</Alert>
+      )}
+      {option && (
+        <Box sx={{ display: "flex", justifyContent: "flex-end", mb: 1 }}>
+          <TextField select size="small" label="Chart style" value={chartType}
+            onChange={(e) => setChartType(e.target.value)} sx={{ minWidth: 170 }}>
+            <MenuItem value="stacked">Stacked bars (composition)</MenuItem>
+            <MenuItem value="grouped">Grouped bars (compare)</MenuItem>
+            <MenuItem value="line">Lines (trend)</MenuItem>
+          </TextField>
+        </Box>
+      )}
+      {option && <ReactECharts option={option} style={{ height: 440 }} notMerge />}
+
+      {/* group definitions */}
+      {stackValues.length > 0 && (
+        <Box sx={{ mt: 2 }}>
+          <Typography variant="subtitle2" gutterBottom>What each “{dimLabel(stackDimObj)}” means (ontology / TS):</Typography>
+          <Grid container spacing={1}>
+            {stackValues.map((s, i) => {
+              const info = s.fullIri ? terms[s.fullIri] : null;
+              return (
+                <Grid item xs={12} sm={6} md={4} key={i}>
+                  <Card variant="outlined" sx={{ height: "100%", borderLeft: `4px solid ${PALETTE[i % PALETTE.length]}` }}>
+                    <CardContent sx={{ py: 1.5 }}>
+                      <Typography variant="subtitle2">{info?.label || s.label}</Typography>
+                      <Typography variant="caption" color="text.secondary">{info?.description || "…"}</Typography>
+                      {s.fullIri && <Box sx={{ mt: 0.5 }}><Link href={s.fullIri} target="_blank" variant="caption">ontology term ↗</Link></Box>}
+                    </CardContent>
+                  </Card>
+                </Grid>
+              );
+            })}
+          </Grid>
+        </Box>
+      )}
+
+      {/* HOW IT WORKS */}
+      <Accordion sx={{ mt: 2 }} disableGutters>
+        <AccordionSummary expandIcon={<ExpandMoreIcon />}>
+          <Typography variant="subtitle2">How this works — from raw values to ontology terms</Typography>
+        </AccordionSummary>
+        <AccordionDetails>
+          <Alert severity="info" sx={{ mb: 2 }}>
+            Dimensions, predicates and value IRIs come from <code>/oekg/registry/</code>; labels and
+            definitions are loaded from the TIB Terminology Service. Works for any annotated table.
+          </Alert>
+          {rawString ? (
+            <>
+              <Typography variant="body2" color="text.secondary">Example: one opaque <code>iamc_full_string</code> value —</Typography>
+              <Box sx={{ fontFamily: "monospace", my: 1, p: 1, bgcolor: "grey.100", borderRadius: 1 }}>{rawString}</Box>
+              <Typography variant="body2" color="text.secondary" sx={{ mb: 1 }}>…decomposed into ontology terms (hover for definition, click to open):</Typography>
+              <Box sx={{ display: "flex", flexWrap: "wrap", gap: 1 }}>
+                {segments.map(({ seg, match }, i) => (
+                  <Box key={i} sx={{ textAlign: "center" }}>
+                    <Typography variant="caption" display="block" color="text.secondary">{match ? titleCase(match.dimension) : "unmapped"}</Typography>
+                    {match && match.iri
+                      ? <TermChip info={terms[match.iri]} fallbackLabel={match.label || seg} fullIri={expandCurie(registry, match.iri)} color={PALETTE[i % PALETTE.length]} />
+                      : <Chip label={seg} size="small" variant="outlined" />}
+                  </Box>
+                ))}
+              </Box>
+            </>
+          ) : (
+            <Typography variant="body2" color="text.secondary">
+              This dataset has no packed IAMC string; its dimensions come directly from annotated columns.
+            </Typography>
+          )}
+          {lastQuery && (
+            <details style={{ marginTop: 16 }}>
+              <summary>SPARQL (what ran under the hood)</summary>
+              <pre style={{ whiteSpace: "pre-wrap", fontSize: 12 }}>{lastQuery}</pre>
+            </details>
+          )}
+        </AccordionDetails>
+      </Accordion>
+    </Box>
+  );
+}
diff --git a/factsheet/frontend/src/components/comparison/registryQuery.js b/factsheet/frontend/src/components/comparison/registryQuery.js
new file mode 100644
index 000000000..63e01b4c1
--- /dev/null
+++ b/factsheet/frontend/src/components/comparison/registryQuery.js
@@ -0,0 +1,188 @@
+// SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+//
+// SPDX-License-Identifier: AGPL-3.0-or-later
+
+// P1/P2 of the registry-driven refactor (see Obsidian "10 - Frontend Refactor
+// Plan"). Pure helpers — no React — that build SPARQL from the Dimension
+// Property Registry contract served by GET /oekg/registry/. Unit-testable.
+//
+// Contract shape (oekg/registry/loader.py):
+//   { namespaces, row_anchor, generic_super_property, dimensions: [
+//       { key, concept, predicate, object_kind, datatype, value_space,
+//         values: [{ code, iri, label }] } ] }
+//
+// KEY IDEA — disambiguating shared predicates:
+//   IAMC dimensions currently share the generic `is about` predicate
+//   (oeo:IAO_0000136). Selecting `?s <pred> ?technology` alone would bind to
+//   EVERY annotated concept on the row. Because the token dictionary assigns
+//   each value to exactly one dimension, we isolate a dimension by constraining
+//   its variable to that dimension's enum IRIs:
+//       ?s oeo:IAO_0000136 ?technology .
+//       FILTER(?technology IN ( <…technology IRIs…> ))
+//   This needs the token IRIs filled (resolve_terms.py) but NO new predicates.
+
+const TABLE_PRED = "oeo:OEO_00000504"; // table-name predicate (row anchor)
+const VALUE_KEY = "quantity_value"; // dimension carrying the numeric measure
+
+export function prefixHeader(registry) {
+  return Object.entries(registry.namespaces || {})
+    .map(([p, iri]) => `PREFIX ${p}: <${iri}>`)
+    .join("\n");
+}
+
+export function expandCurie(registry, curie) {
+  if (!curie || curie.startsWith("http") || !curie.includes(":")) return curie;
+  const i = curie.indexOf(":");
+  const prefix = curie.slice(0, i);
+  const local = curie.slice(i + 1);
+  const base = (registry.namespaces || {})[prefix];
+  return base ? `${base}${local}` : curie;
+}
+
+// Full http IRI -> <...>; CURIE (prefix declared in the header) -> verbatim.
+export function sparqlTerm(iriOrCurie) {
+  if (!iriOrCurie) return iriOrCurie;
+  return iriOrCurie.startsWith("http") ? `<${iriOrCurie}>` : iriOrCurie;
+}
+
+// Predicates used by >1 iri-dimension (i.e. the generic `is about`) — these
+// need enum isolation.
+export function sharedPredicates(registry) {
+  const counts = {};
+  for (const d of registry.dimensions || []) {
+    if (d.object_kind !== "iri") continue;
+    counts[d.predicate] = (counts[d.predicate] || 0) + 1;
+  }
+  return new Set(Object.keys(counts).filter((p) => counts[p] > 1));
+}
+
+const enumTerms = (dim) =>
+  (dim.values || []).filter((v) => v.iri).map((v) => sparqlTerm(v.iri));
+
+const tableFilter = (tables) =>
+  tables && tables.length
+    ? `FILTER(?table_name IN (${tables.map((t) => `"${t}"`).join(", ")})) .`
+    : "";
+
+// Distinct values of one dimension actually present in the selected tables.
+export function dimensionValuesQuery({ registry, dim, tables = [] }) {
+  let isolate = "";
+  if (dim.object_kind === "iri" && sharedPredicates(registry).has(dim.predicate)) {
+    const set = enumTerms(dim);
+    if (set.length) isolate = `FILTER(?v IN (${set.join(", ")})) .`;
+  }
+  return `${prefixHeader(registry)}
+SELECT DISTINCT ?v ?table_name WHERE {
+  ?s ${dim.predicate} ?v . ?s ${TABLE_PRED} ?table_name .
+  ${isolate}
+  ${tableFilter(tables)}
+}`;
+}
+
+// Units present in a table, most common first. If `dim` is given, only units
+// that CO-OCCUR with that dimension are returned (e.g. units that actually apply
+// when breaking down by technology) — keeps the unit list relevant + small.
+export function unitFrequencyQuery({ registry, table, dim }) {
+  const unitDim = (registry.dimensions || []).find((d) => d.key === "unit");
+  const pred = unitDim ? unitDim.predicate : "oeo:OEO_00040010";
+  let cond = "";
+  if (dim && dim.key !== "unit") {
+    let iso = "";
+    if (dim.object_kind === "iri" && sharedPredicates(registry).has(dim.predicate)) {
+      const set = enumTerms(dim);
+      if (set.length) iso = ` FILTER(?dv IN (${set.join(", ")}))`;
+    }
+    cond = ` ?s ${dim.predicate} ?dv .${iso}`;
+  }
+  return `${prefixHeader(registry)}
+SELECT ?v (COUNT(?s) AS ?c) WHERE {
+  ?s ${TABLE_PRED} ?t . FILTER(?t = "${table}") ?s ${pred} ?v .${cond}
+} GROUP BY ?v ORDER BY DESC(?c)`;
+}
+
+// Distinct values of a dimension by frequency (most common first), optionally
+// scoped by another (literal) dimension = value — e.g. quantities in a table, or
+// the units that occur FOR a chosen quantity (unit follows the quantity).
+export function valueFrequencyQuery({ registry, table, dim, scopeDim, scopeValue }) {
+  let scope = "";
+  if (scopeDim && scopeValue) {
+    const esc = String(scopeValue).replace(/"/g, '\\"');
+    scope = ` ?s ${scopeDim.predicate} ?sv . FILTER(STR(?sv) = "${esc}")`;
+  }
+  return `${prefixHeader(registry)}
+SELECT ?v (COUNT(?s) AS ?c) WHERE {
+  ?s ${TABLE_PRED} ?t . FILTER(?t = "${table}") ?s ${dim.predicate} ?v .${scope}
+} GROUP BY ?v ORDER BY DESC(?c)`;
+}
+
+// Cheap existence check: does this table populate this dimension at all?
+// Used to show only dimensions/presets that will actually return data.
+export function dimensionAskQuery({ registry, table, dim }) {
+  let iso = "";
+  if (dim.object_kind === "iri" && sharedPredicates(registry).has(dim.predicate)) {
+    const set = enumTerms(dim);
+    if (set.length) iso = ` FILTER(?v IN (${set.join(", ")}))`;
+  }
+  return `${prefixHeader(registry)}
+ASK { ?s ${TABLE_PRED} ?t . FILTER(?t = "${table}") ?s ${dim.predicate} ?v .${iso} }`;
+}
+
+// The comparison query: select the numeric value + the chosen dimension axes,
+// filtered by the user's selections.
+//   dims:    array of dimension keys to project (e.g. ["technology","scenario_year"])
+//   filters: { [dimKey]: code[] } user selections
+export function buildComparisonQuery({ registry, tables = [], filters = {}, dims = [] }) {
+  const shared = sharedPredicates(registry);
+  const byKey = Object.fromEntries((registry.dimensions || []).map((d) => [d.key, d]));
+  const selected = dims.map((k) => byKey[k]).filter(Boolean);
+
+  const valueDim = byKey[VALUE_KEY];
+  const patterns = [
+    `?s ${valueDim ? valueDim.predicate : "oeo:OEO_00140178"} ?value .`,
+    `?s ${TABLE_PRED} ?table_name .`,
+  ];
+  const vars = [];
+  const filterLines = [];
+
+  const tf = tableFilter(tables);
+  if (tf) filterLines.push(tf);
+
+  for (const d of selected) {
+    if (d.key === VALUE_KEY) continue;
+    const v = `?${d.key}`;
+    vars.push(v);
+    patterns.push(`?s ${d.predicate} ${v} .`);
+
+    // isolate shared-predicate (generic is-about) dims by their enum set
+    if (d.object_kind === "iri" && shared.has(d.predicate)) {
+      const set = enumTerms(d);
+      if (set.length) patterns.push(`FILTER(${v} IN (${set.join(", ")})) .`);
+    }
+
+    const codes = filters[d.key];
+    if (codes && codes.length) {
+      const terms = codes.map((code) => {
+        if (d.object_kind === "iri") {
+          const val = (d.values || []).find((x) => x.code === code);
+          return sparqlTerm(val ? val.iri : code);
+        }
+        return `"${code}"`;
+      });
+      filterLines.push(`FILTER(${v} IN (${terms.join(", ")})) .`);
+    }
+  }
+
+  return `${prefixHeader(registry)}
+SELECT DISTINCT ?s ?value ?table_name ${vars.join(" ")} WHERE {
+  ${patterns.join("\n  ")}
+  ${filterLines.join("\n  ")}
+}`;
+}
+
+// Map a result's full IRI back to a human label for a given dimension.
+export function labelForIri(registry, dim, fullIri) {
+  for (const v of dim.values || []) {
+    if (v.iri && expandCurie(registry, v.iri) === fullIri) return v.label || v.code;
+  }
+  return fullIri;
+}
\ No newline at end of file
diff --git a/factsheet/frontend/src/components/comparison/tibTerms.js b/factsheet/frontend/src/components/comparison/tibTerms.js
new file mode 100644
index 000000000..cd91c87a1
--- /dev/null
+++ b/factsheet/frontend/src/components/comparison/tibTerms.js
@@ -0,0 +1,76 @@
+// SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+//
+// SPDX-License-Identifier: AGPL-3.0-or-later
+
+// Resolve an ontology IRI to { label, description } via the TIB Terminology
+// Service. Extracted from quantitativeView.jsx so the registry-driven view can
+// reuse the exact same resolution (terms → individuals → properties) and cache.
+// OEO references all terms under its own base, so we normalise to the oeo IRI
+// from the short form before querying.
+
+import axios from "axios";
+
+const cache = {};
+
+export async function resolveTerm(iri) {
+  if (!iri) return null;
+  if (cache[iri]) return cache[iri];
+
+  const shortForm = iri.split("/").pop().split(":").pop();
+  const officialIri = `https://openenergyplatform.org/ontology/oeo/${shortForm}`;
+  const encoded = encodeURIComponent(officialIri);
+  const baseUrl =
+    import.meta.env.VITE_TSS_API_BASE?.replace(/\/$/, "") ||
+    "https://api.terminology.tib.eu/api";
+  const ontology = import.meta.env.VITE_TSS_DEFAULT_ONTOLOGY || "oeo";
+
+  const tryEndpoint = async (endpoint) => {
+    try {
+      const res = await axios.get(
+        `${baseUrl}/ontologies/${ontology}/${endpoint}?iri=${encoded}`
+      );
+      const items = res.data?._embedded?.[endpoint];
+      if (items && items.length > 0) {
+        const item = items[0];
+        return {
+          iri,
+          label: item.label || shortForm,
+          description:
+            item.description && item.description.length > 0
+              ? item.description.join(" ")
+              : "No official definition provided in the ontology.",
+          type: endpoint,
+        };
+      }
+    } catch (e) {
+      /* fall through */
+    }
+    return null;
+  };
+
+  let info =
+    (await tryEndpoint("terms")) ||
+    (await tryEndpoint("individuals")) ||
+    (await tryEndpoint("properties"));
+  if (!info) {
+    info = {
+      iri,
+      label: shortForm,
+      description: "Term not found in Terminology Service.",
+      type: "unknown",
+    };
+  }
+  cache[iri] = info;
+  return info;
+}
+
+// Resolve many IRIs; returns a map { iri: info }.
+export async function resolveTerms(iris) {
+  const out = {};
+  await Promise.all(
+    [...new Set(iris.filter(Boolean))].map(async (iri) => {
+      out[iri] = await resolveTerm(iri);
+    })
+  );
+  return out;
+}
\ No newline at end of file
diff --git a/factsheet/frontend/src/components/comparison/useRegistry.js b/factsheet/frontend/src/components/comparison/useRegistry.js
new file mode 100644
index 000000000..fb61d58c4
--- /dev/null
+++ b/factsheet/frontend/src/components/comparison/useRegistry.js
@@ -0,0 +1,50 @@
+// SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+//
+// SPDX-License-Identifier: AGPL-3.0-or-later
+
+// P0 of the registry-driven refactor (see Obsidian "10 - Frontend Refactor Plan").
+// Fetches the Dimension Property Registry contract from GET /oekg/registry/ once
+// per page load and caches it. The contract is the shared vocabulary the
+// comparison view uses to build filters + dynamic SPARQL (no hardcoded terms).
+
+import { useEffect, useState } from "react";
+import axios from "axios";
+import conf from "../../conf.json";
+
+let _cache = null; // module-scope: one fetch per page load
+let _inflight = null;
+
+export async function fetchRegistry() {
+  if (_cache) return _cache;
+  if (!_inflight) {
+    _inflight = axios
+      .get(conf.dimensionRegistry)
+      .then((res) => {
+        _cache = res.data;
+        return _cache;
+      })
+      .finally(() => {
+        _inflight = null;
+      });
+  }
+  return _inflight;
+}
+
+export default function useRegistry() {
+  const [registry, setRegistry] = useState(_cache);
+  const [error, setError] = useState(null);
+
+  useEffect(() => {
+    let active = true;
+    if (!_cache) {
+      fetchRegistry()
+        .then((r) => active && setRegistry(r))
+        .catch((e) => active && setError(e));
+    }
+    return () => {
+      active = false;
+    };
+  }, []);
+
+  return { registry, loading: !registry && !error, error };
+}
\ No newline at end of file
diff --git a/factsheet/frontend/src/components/comparisonBoardMain.tsx b/factsheet/frontend/src/components/comparisonBoardMain.tsx
index f81dbb15a..e07d45116 100644
--- a/factsheet/frontend/src/components/comparisonBoardMain.tsx
+++ b/factsheet/frontend/src/components/comparisonBoardMain.tsx
@@ -19,6 +19,7 @@ import BreadcrumbsNavGrid from "../styles/oep-theme/components/breadcrumbsNaviga
 // Import our new sub-components
 import QualitativeView from "./comparison/qualitativeView.jsx";
 import QuantitativeView from "./comparison/quantitativeView.jsx";
+import RegistryComparison from "./comparison/RegistryComparison.jsx";
 
 const ComparisonBoardMain = ({ params }) => {
   const [scenarios, setScenarios] = useState([]);
@@ -93,6 +94,9 @@ const ComparisonBoardMain = ({ params }) => {
                   <ToggleButton style={{ width: "250px" }} value="Quantitative">
                     <EqualizerIcon /> Quantitative
                   </ToggleButton>
+                  <ToggleButton style={{ width: "250px" }} value="Registry">
+                    <EqualizerIcon /> Registry (beta)
+                  </ToggleButton>
                 </ToggleButtonGroup>
               </Grid>
               <Grid item xs={2}></Grid>
@@ -106,6 +110,7 @@ const ComparisonBoardMain = ({ params }) => {
           {alignment === "Quantitative" && (
             <QuantitativeView scenarios={scenarios} />
           )}
+          {alignment === "Registry" && <RegistryComparison />}
         </Container>
       </Grid>
     )
diff --git a/factsheet/frontend/src/conf.json b/factsheet/frontend/src/conf.json
index fad09fc89..a3bf54443 100644
--- a/factsheet/frontend/src/conf.json
+++ b/factsheet/frontend/src/conf.json
@@ -1,5 +1,6 @@
 {
   "toep": "/",
   "obdi": "/api/oevkg-query",
-  "oekgQueryFilter": "oekg/filter-by-criteria/"
+  "oekgQueryFilter": "oekg/filter-by-criteria/",
+  "dimensionRegistry": "/oekg/registry/"
 }
diff --git a/oekg/registry/README.md b/oekg/registry/README.md
new file mode 100644
index 000000000..48e07c038
--- /dev/null
+++ b/oekg/registry/README.md
@@ -0,0 +1,97 @@
+# Dimension Property Registry
+
+The **single source of truth** for the harmonized RDF vocabulary used by the
+scenario comparison service. It maps each comparable _dimension_ (region, gas,
+scenario type, technology, …) to the **predicate** used in generated triples,
+the **object kind** (IRI vs literal), and — for controlled vocabularies — the
+**value space** (`code → IRI`).
+
+## Why it exists
+
+- oemetadata `isAbout` only gives a **concept (a class)**; an RDF triple needs a
+  **predicate (an object property)**. The Terminology Service can't derive that
+  link yet, so it must live as data.
+- The mapping generator (separate repo) currently **hardcodes** this
+  `concept → predicate` lookup.
+- The comparison UI
+  (`factsheet/frontend/src/components/comparison/quantitativeView.jsx`)
+  **hardcodes** the same predicates on the query side.
+
+This file replaces _both_ hardcodings. Harmonization only works if every
+dataset's mapping and the UI use the **same** predicates and value IRIs — that
+is exactly what this registry guarantees.
+
+## Files
+
+| File                               | Purpose                                                                                                                                                                                     |
+| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `dimension_property_registry.yaml` | The registry itself (source of truth). Provenance-tagged: `[confirmed]` / `[verify]` / `[ts]`.                                                                                              |
+| `loader.py`                        | Loads the YAML → the `registry.json` **contract dict** (`load_registry()`), inlines value spaces per dimension, + accessors (`predicate_for`, `expand`). The shared API for both consumers. |
+| `validate_registry.py`             | Structural validator + Phase-1 work-list report. No Django deps; CI-ready.                                                                                                                  |
+| `resolve_terms.py`                 | Resolves labels → IRIs via the TIB Terminology Service (so term IRIs aren't hand-maintained).                                                                                               |
+
+## Consumers (one shared contract)
+
+The registry is an **input**, not a generator output. `loader.build_contract()`
+produces one dict consumed by both:
+
+- **Frontend** ← `GET /oekg/registry/`
+  (`oekg/views.py: dimension_registry_view`) → builds dynamic SPARQL + filter
+  UI.
+- **Mapping generator** (imported by OEP as a Python package) ← OEP calls
+  `generate(table_oemetadata: dict, registry: dict) -> obda_str` with this same
+  dict in-process; the generator never imports OEP internals.
+
+```bash
+python oekg/registry/loader.py        # print the registry.json contract
+```
+
+## Curated here vs resolved from the TS
+
+The registry is **only** the `dimension → predicate` map (+
+object_kind/datatype) — a modelling choice the Terminology Service cannot derive
+(property-by-label search is unreliable). All **term IRIs** (the `value_spaces`)
+are resolvable from the TS and should be treated as a re-runnable cache, not
+authored truth:
+
+```bash
+python oekg/registry/resolve_terms.py "battery electric vehicle"
+python oekg/registry/resolve_terms.py --fill-iamc-tokens
+```
+
+Per-dataset `value → IRI` mappings ultimately belong in oemetadata
+`valueReference`, filled by the resolver and human-confirmed where ambiguous
+(codes like `CO2` return several candidates). See the Obsidian note _08 -
+Terminology Service_.
+
+## Usage
+
+```bash
+python oekg/registry/validate_registry.py
+```
+
+Exit code `0` = structurally valid (TODOs are warnings); `1` = structural error.
+
+## Consumers
+
+- **Mapping generator (separate repo):**
+  `property_url = registry.predicate_for(concept_or_dimension)`, then
+  `f'{subject_url} {property_url} <{object_iri}> .'`. Vendor or fetch this file.
+- **Comparison UI:** should read predicates from here instead of hardcoding them
+  (Phase 2).
+
+## Conventions
+
+- Specific predicates should be declared `rdfs:subPropertyOf` the
+  `generic_super_property` (`obo:IAO_0000136`, "is about"), so a generic query
+  catches everything and typed queries still enable dimensional grouping.
+- **Namespace hygiene (correctness gate 10):** one canonical IRI form for
+  `oeo:`/`oekg:` must be used for _every_ predicate and object. See the warning
+  at the top of the YAML — the existing `.obda` files mix
+  `http://openenergy-platform.org/…` and `https://openenergyplatform.org/…`.
+
+## Background docs
+
+- `scripts/oevkg/sem_mapping/ANNOTATION_AND_MAPPING_DESIGN.md` (§5)
+- Obsidian vault: _04 - Annotation Contract_, _05 - Generator Correctness_,
+  _07 - Concept to Predicate_
diff --git a/oekg/registry/__init__.py b/oekg/registry/__init__.py
new file mode 100644
index 000000000..85dae9538
--- /dev/null
+++ b/oekg/registry/__init__.py
@@ -0,0 +1,4 @@
+"""
+SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+SPDX-License-Identifier: AGPL-3.0-or-later
+"""  # noqa: 501
diff --git a/oekg/registry/dimension_property_registry.yaml b/oekg/registry/dimension_property_registry.yaml
new file mode 100644
index 000000000..af0058525
--- /dev/null
+++ b/oekg/registry/dimension_property_registry.yaml
@@ -0,0 +1,295 @@
+# =============================================================================
+# Dimension Property Registry
+# -----------------------------------------------------------------------------
+# Single source of truth for the harmonized RDF vocabulary used by the scenario
+# comparison service. It maps each comparable DIMENSION to:
+#   - the CONCEPT it annotates (the class an oemetadata `isAbout` resolves to),
+#   - the PREDICATE used in the generated triple (the object property),
+#   - whether the object is an IRI or a literal (+ datatype),
+#   - and, for controlled vocabularies, the value space (code -> IRI).
+#
+# Read by BOTH:
+#   * the mapping generator (separate repo) -> replaces its hardcoded
+#     `concept -> predicate` lookup; supplies `property_url`.
+#   * the comparison UI (factsheet/frontend/.../quantitativeView.jsx) -> today
+#     these predicates are hardcoded there; the UI should read them from here.
+#
+# RESOLUTION MODEL (what is curated here vs resolved from the TIB TS):
+#   * CURATED HERE  -> the `dimensions` list = dimension -> predicate association.
+#     This is a MODELLING choice, not ontology data: the TIB Terminology Service
+#     cannot derive "which object property attaches a data point to this concept"
+#     (property-by-label search is unreliable). Small + stable -> fine to maintain.
+#   * RESOLVED FROM TS -> all term/concept IRIs (the `value_spaces` below). Do NOT
+#     hand-author these as truth. Use oekg/registry/resolve_terms.py against
+#     https://api.terminology.tib.eu (e.g. /api/search?q=<label>&ontology=oeo).
+#     The block below is a confirmed, re-runnable CACHE, tagged [ts] with its IRI.
+#     Per-dataset cell-value -> IRI mappings ultimately live in oemetadata
+#     `valueReference`, filled by the same resolver and human-confirmed if ambiguous.
+#
+# Provenance tags on every entry:
+#   [confirmed] verbatim from quantitativeView.jsx / *.obda / table oemetadata.
+#   [verify]    inferred or partially known — confirm before relying on it.
+#   [ts]        resolved from the TIB Terminology Service (re-runnable cache).
+#
+# Docs: scripts/oevkg/sem_mapping/ANNOTATION_AND_MAPPING_DESIGN.md  (§5)
+#       Obsidian: "04 - Annotation Contract", "07 - Concept to Predicate",
+#                 "08 - Terminology Service"
+# =============================================================================
+
+version: 0.1.0
+updated: 2026-06-12
+status: seed # Phase 1 — confirm [verify]/[proposed] rows, then promote
+
+# -----------------------------------------------------------------------------
+# Namespaces
+# -----------------------------------------------------------------------------
+# NAMESPACE RULE (correctness gate 10):
+# OEO references ALL terms — native AND imported — under the oeo namespace base
+# https://openenergyplatform.org/ontology/oeo/ . The source ontology is carried in
+# the LOCAL ID prefix: OEO_* (native), IAO_*/BFO_* (imported from IAO/BFO),
+# UO_* (units). So e.g. "is about" = oeo:IAO_0000136. Evidence: mapping.obda itself
+# writes the anchor class as `oeo:IAO_0000027`, and the UI uses this same oeo base.
+# One base -> use `oeo:` for everything; the local-ID prefix signals provenance.
+# The real bug to avoid: the SAME term written two ways. mapping.obda writes
+# gas-species OBJECTS as http://openenergy-platform.org/ontology/oeo/OEO_xxxx
+# (the OLD http+hyphen base) while predicates use the https base -> joins silently
+# fail. ACTION: rewrite those objects to the oeo base + regenerate.
+namespaces:
+  oeo: https://openenergyplatform.org/ontology/oeo/ # [confirmed] base for ALL terms (OEO_*, IAO_*, UO_*, BFO_*)
+  oekg: https://openenergyplatform.org/ontology/oeo/oekg/ # [confirmed]
+  llc: https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/ # [confirmed]
+
+# -----------------------------------------------------------------------------
+# Row anchor — how each source row becomes a subject
+# -----------------------------------------------------------------------------
+row_anchor:
+  iri_template: "oekg:data-descriptor/{table}/{id}" # [confirmed] from all *.obda
+  class: oeo:IAO_0000027 # [confirmed] data item / ICE
+  table_name_predicate: oeo:OEO_00000504 # [confirmed] string literal "{table}"
+  requires: { primary_key: id, unique: true } # correctness gate 1
+
+# Generic super-property. Specific predicates below SHOULD be declared
+# rdfs:subPropertyOf this, so a generic "is about" query catches everything
+# while typed predicates still enable dimensional grouping.
+generic_super_property: oeo:IAO_0000136 # "is about"  [confirmed] (interim predicate used by generator)
+
+# -----------------------------------------------------------------------------
+# Dimensions
+# -----------------------------------------------------------------------------
+dimensions:
+  - key: spatial_region
+    concept: oeo:OEO_00020032 # "study region"            [confirmed] (ariadne `region` isAbout)
+    predicate: oeo:OEO_00020221 # "has considered region"   [confirmed] (UI + mapping.obda)
+    object_kind: iri
+    value_space: iso3166_lcc
+
+  - key: scenario_type
+    concept: oeo:OEO_00000364 # "scenario"                [confirmed] (ariadne `scenario` isAbout)
+    predicate: oeo:OEO_00020226 #                           [confirmed] (UI + mapping.obda)
+    object_kind: iri
+    value_space: scenario_type_codes
+
+  # The scenario NAME/identifier (e.g. KN2045_Mix, ExPol) — distinct from the CRF
+  # scenario_type (WEM/WAM/WOM). Dataset-specific free labels → literal.
+  - key: scenario
+    concept: null # a named scenario instance; no global IRI
+    predicate: oekg:scenario # [provisional] scenario name as literal
+    object_kind: literal
+    datatype: xsd:string
+
+  - key: greenhouse_gas
+    concept: oeo:OEO_00000020 # "greenhouse gas"          [confirmed] OEO 2.12 oeo-full.owl
+    predicate: oeo:OEO_00010121 #                           [confirmed] (UI + mapping.obda; ObjectProperty)
+    object_kind: iri
+    value_space: ghg_species_codes
+
+  - key: sector_division
+    concept: oeo:OEO_00030022 # "sector division"         [confirmed] OEO 2.12 (used 72x in OEKG)
+    predicate: oeo:has_sector_division # named prop, NOT OEO_*  [confirmed] (UI + mapping.obda)
+    object_kind: literal
+    datatype: xsd:string # NOTE: currently a free string, not an IRI — candidate to upgrade
+
+  - key: scenario_year
+    concept: oeo:OEO_00020097 # "scenario year"           [confirmed] (ariadne `scenario_year` isAbout)
+    predicate: oeo:OEO_00020224 #                           [confirmed] (UI + mapping.obda)
+    object_kind: literal
+    datatype: xsd:gYear # [verify] mapping.obda emits plain "{year}" — pick gYear vs integer
+
+  - key: unit
+    concept: oeo:UO_0000000 # "unit"  [confirmed] NB ariadne isAbout used obo PURL form -> normalize to oeo base
+    predicate: oeo:OEO_00040010 #                           [confirmed] (UI + mapping.obda)
+    object_kind: literal
+    datatype: xsd:string
+
+  - key: quantity_value
+    concept: oeo:OEO_00000350 # "quantity value"          [confirmed] (ariadne `value` isAbout)
+    predicate: oeo:OEO_00140178 # emission/quantity value   [confirmed] (UI + mapping.obda)
+    object_kind: literal
+    datatype: xsd:double
+
+  # --- IAMC variable dimensions (token-mapping) -----------------------------
+  # These come from decomposing the IAMC `Variable` path. The token dictionary
+  # (per-dataset oemetadata valueReference) supplies token -> {IRI, dimension}.
+  # DECISION (2026-06-12): use the generic `is about` (oeo:IAO_0000136) predicate
+  # for ALL IAMC tokens for now (Philipp's interim). The `key`/`dimension` is kept
+  # so we can later REFINE to typed sub-properties (hasTransportMode, hasTechnology,
+  # …) without re-curating the token dictionary. Each refined predicate should be
+  # rdfs:subPropertyOf oeo:IAO_0000136.
+  # FIXED 2026-06-12: quantity is the TOP IAMC segment (the `quantity_variable`
+  # column: Final Energy, Investment, Emissions…), NOT a token anywhere in the
+  # path. Mapping from the column gives ONE quantity per row (no double-count).
+  # IAMC quantities aren't cleanly in OEO (e.g. "final energy" ≠ "final energy
+  # carrier"), so we keep the raw label as a literal — upgrade to IAMC
+  # common-definitions IRIs later.
+  - key: quantity_kind
+    concept: oeo:OEO_00000435 # "variable"  [confirmed]
+    predicate: oekg:quantity_kind # [provisional] literal label, sourced from quantity_variable
+    object_kind: literal
+    datatype: xsd:string
+
+  - key: sector
+    predicate: oeo:IAO_0000136 # is about (interim; refine -> hasSector)
+    object_kind: iri
+    value_space: iamc_tokens
+
+  - key: transport_mode
+    predicate: oeo:IAO_0000136 # is about (interim; refine -> hasTransportMode)
+    object_kind: iri
+    value_space: iamc_tokens
+
+  - key: technology
+    predicate: oeo:IAO_0000136 # is about (interim; refine -> hasTechnology)
+    object_kind: iri
+    value_space: iamc_tokens
+
+  - key: qualifier
+    predicate: oeo:IAO_0000136 # is about (interim; refine -> hasQualifier); "additional" -> increase of role
+    object_kind: iri
+    value_space: iamc_tokens
+
+# -----------------------------------------------------------------------------
+# Value spaces — controlled vocabularies (code in the DB -> object IRI)
+# These are a CACHE. IRIs are resolvable from the TIB TS (resolve_terms.py); the
+# [confirmed] ones below were already present in the existing .obda mappings.
+# -----------------------------------------------------------------------------
+value_spaces:
+  # [confirmed] from scripts/oevkg/sem_mapping/mapping.obda
+  ghg_species_codes:
+    CO2: oeo:OEO_00000006 # carbon dioxide
+    CH4: oeo:OEO_00000025 # methane
+    N2O: oeo:OEO_00000027 # nitrous oxide
+    NF3: oeo:OEO_00000026 # nitrogen trifluoride
+    HFC: oeo:OEO_00000219 # hydrofluorocarbon
+    PFC: oeo:OEO_00000322 # perfluorocarbon
+    SF6: oeo:OEO_00000038 # sulphur hexafluoride
+
+  # [confirmed] from mapping.obda
+  scenario_type_codes:
+    WEM: oeo:OEO_00020311 # with existing measures
+    WAM: oeo:OEO_00020312 # with additional measures
+    WOM: oeo:OEO_00020310 # without measures
+
+  # [confirmed] code -> LCC English label from mapping.obda.
+  # Quirks preserved verbatim: EL & GR both -> Greece; GB & UK both -> UK.
+  iso3166_lcc:
+    _convention: "llc:<EnglishName> (ISO-3166-1 alpha-2 code -> LCC label)"
+    AT: llc:Austria
+    BE: llc:Belgium
+    BG: llc:Bulgaria
+    CH: llc:Switzerland
+    CY: llc:Cyprus
+    CZ: llc:Czechia
+    DE: llc:Germany
+    DEU: llc:Germany # ISO-3166-1 alpha-3 (ariadne uses 3-letter codes)
+    DK: llc:Denmark
+    EE: llc:Estonia
+    EL: llc:Greece
+    ES: llc:Spain
+    FI: llc:Finland
+    FR: llc:France
+    GB: llc:UnitedKingdomOfGreatBritainAndNorthernIreland
+    GR: llc:Greece
+    HR: llc:Croatia
+    HU: llc:Hungary
+    IE: llc:Ireland
+    IS: llc:Iceland
+    IT: llc:Italy
+    LT: llc:Lithuania
+    LU: llc:Luxembourg
+    LV: llc:Latvia
+    MT: llc:Malta
+    NL: llc:Netherlands
+    "NO": llc:Norway # quoted: bare NO is YAML-1.1 boolean false
+    PL: llc:Poland
+    PT: llc:Portugal
+    RO: llc:Romania
+    SE: llc:Sweden
+    SI: llc:Slovenia
+    SK: llc:Slovakia
+    UK: llc:UnitedKingdomOfGreatBritainAndNorthernIreland
+
+  # IAMC token dictionary (Phase 1, step 2). token (path segment) -> {iri, dimension, name}.
+  # `name` is the human label a curator resolves via the TS; `iri` is the resolved [ts] result.
+  # Resolve with: python oekg/registry/resolve_terms.py  (reads the names, fills iris).
+  # IRIs below tagged [ts] were resolved live against api.terminology.tib.eu (ontology=oeo).
+  iamc_tokens:
+    bev: {
+        iri: "oeo:OEO_00010024",
+        dimension: technology,
+        name: "battery electric vehicle",
+      } # [ts]
+    fcev: {
+        iri: "oeo:OEO_00010025",
+        dimension: technology,
+        name: "fuel cell electric vehicle",
+      } # [ts]
+    ice: {
+        iri: "oeo:OEO_00000240",
+        dimension: technology,
+        name: "internal combustion vehicle",
+      } # [ts]
+    phev: {
+        iri: "oeo:OEO_00010030",
+        dimension: technology,
+        name: "plug-in hybrid electric vehicle",
+      } # [ts]
+    energy demand: {
+        iri: "oeo:OEO_00140146",
+        dimension: quantity_kind,
+        name: "energy demand",
+      } # [ts]
+    investment: {
+        iri: "oeo:OEO_00020167",
+        dimension: quantity_kind,
+        name: "investment cost",
+      } # [ts]
+    transportation: {
+        iri: "oeo:OEO_00000422",
+        dimension: sector,
+        name: "transport sector",
+      } # [ts]
+    bus: { iri: "oeo:OEO_00010277", dimension: transport_mode, name: "bus" } # [ts]
+    rail: { iri: "oeo:OEO_00010280", dimension: transport_mode, name: "train" } # [ts]
+    truck: { iri: "oeo:OEO_00010278", dimension: transport_mode, name: "truck" } # [ts]
+    overhead line: {
+        iri: "oeo:OEO_00000047",
+        dimension: technology,
+        name: "AC-line",
+      } # [ts] matched "AC-line"
+    domestic aviation: {
+        iri: "oeo:OEO_00010059",
+        dimension: transport_mode,
+        name: "CRF sector (IPCC 2006): domestic aviation",
+      } # [ts] OEO individual
+    domestic navigation: {
+        iri: "oeo:OEO_00010062",
+        dimension: transport_mode,
+        name: "CRF sector (IPCC 2006): domestic navigation",
+      } # [ts] OEO individual
+    # --- UNRESOLVED — need a decision (not in released OEO) ---
+    ldv: {
+        iri: null,
+        dimension: transport_mode,
+        name: "light commercial vehicle",
+      } # not in OEO; mint OR unreleased-ontology vocab
+    additional: { iri: null, dimension: qualifier, name: "increase of role" } # only CommonCore match; qualifier semantics open
diff --git a/oekg/registry/gen_test_mapping.py b/oekg/registry/gen_test_mapping.py
new file mode 100644
index 000000000..43b3d0c75
--- /dev/null
+++ b/oekg/registry/gen_test_mapping.py
@@ -0,0 +1,143 @@
+"""
+SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+SPDX-License-Identifier: AGPL-3.0-or-later
+"""  # noqa: 501
+
+# TEST mapping generator — a stand-in for the (separate-repo) mapping tool, used
+# only to produce OBDA for local testing. It reads the registry contract and a
+# per-table BINDING (which column maps to which dimension) and emits ontop OBDA
+# blocks. NOT the production generator; the binding here is hand-specified
+# (normally it comes from the table's oemetadata).
+#
+# Emits ONLY the mapping blocks (the [PrefixDeclaration]/[MappingDeclaration]
+# wrapper already exists in docker/serviceConfigs/ontop/mapping.obda).
+#
+#   python oekg/registry/gen_test_mapping.py
+#
+# Conventions matched to the existing eu_leg mapping in that file:
+#   * schema "data"; identifiers double-quoted
+#   * IRI objects via prefixed CURIEs (oeo:…); literals as plain "{col}"
+#   * IAMC tokens via exact-segment array containment (DB untouched)
+
+from __future__ import annotations
+
+from oekg.registry.loader import dimension, load_registry
+
+SCHEMA = "data"
+TABLE = "ariadne2_data_with_labels"
+PK = "id"
+IAMC_COL = "iamc_full_string"
+DELIM = " | "
+
+# Per-table binding (would come from oemetadata). column -> dimension key.
+# Literal dimensions: the raw cell value becomes the object literal.
+LITERAL_COLUMNS = {
+    "scenario_year": "scenario_year",
+    "value": "quantity_value",
+    "unit": "unit",
+    "scenario": "scenario",
+}
+# Computed literals: a SQL expression over EXISTING columns becomes the literal.
+#   quantity_kind = the FIRST IAMC segment (the quantity), derived straight from
+#   the variable string — IAMC-only, needs NO extra `quantity_variable` column.
+#   One value per row → no double-count, no "energy demand as a quantity" artefact.
+COMPUTED_LITERALS = {
+    "quantity_kind": "split_part(\"iamc_full_string\", ' | ', 1)",
+}
+# Controlled columns: column -> (dimension key, {db value: object IRI/CURIE}).
+CONTROLLED_COLUMNS = {
+    "region": ("spatial_region", {"DEU": "llc:Germany"}),
+}
+# IAMC dimensions still decomposed from the packed string (position-robust tokens).
+IAMC_DIMENSIONS = ["sector", "transport_mode", "technology", "qualifier"]
+
+
+def _slug(s: str) -> str:
+    return "".join(c if c.isalnum() else "_" for c in s).strip("_")
+
+
+def _block(mapping_id: str, target: str, source: str) -> str:
+    return (
+        f"mappingId       {mapping_id}\n"
+        f"target          {target}\n"
+        f"source          {source}\n"
+    )
+
+
+def generate() -> str:
+    reg = load_registry()
+    src = f'SELECT "{PK}" FROM "{SCHEMA}"."{TABLE}"'
+    subj = f"oekg:data-descriptor/{TABLE}/{{{PK}}}"
+    # NB: ontop's native OBDA parser does NOT allow '#' comment lines inside the
+    # [MappingDeclaration] block — emit only mappingId/target/source blocks.
+    out = []
+
+    # anchor + table name
+    out.append(
+        _block(
+            f"{TABLE}_TargetClass",
+            f'{subj} a oeo:IAO_0000027 ; oeo:OEO_00000504 "{TABLE}"^^xsd:string .',
+            src,
+        )
+    )
+
+    # literal dimensions
+    for col, dim_key in LITERAL_COLUMNS.items():
+        d = dimension(dim_key, reg)
+        out.append(
+            _block(
+                f"{TABLE}_{dim_key}",
+                f'{subj} {d["predicate"]} "{{{col}}}" .',
+                f'SELECT "{PK}", "{col}" FROM "{SCHEMA}"."{TABLE}"',
+            )
+        )
+
+    # computed literals (e.g. quantity_kind = first segment of the variable string)
+    for dim_key, expr in COMPUTED_LITERALS.items():
+        d = dimension(dim_key, reg)
+        out.append(
+            _block(
+                f"{TABLE}_{dim_key}",
+                f'{subj} {d["predicate"]} "{{{dim_key}}}" .',
+                f'SELECT "{PK}", {expr} AS {dim_key} FROM "{SCHEMA}"."{TABLE}"',
+            )
+        )
+
+    # controlled columns: value -> object IRI (WHERE col = value)
+    for col, (dim_key, codemap) in CONTROLLED_COLUMNS.items():
+        d = dimension(dim_key, reg)
+        for code, iri in codemap.items():
+            out.append(
+                _block(
+                    f"{TABLE}_{dim_key}_{_slug(code)}",
+                    f'{subj} {d["predicate"]} {iri} .',
+                    f'SELECT "{PK}" FROM "{SCHEMA}"."{TABLE}" '
+                    f"WHERE \"{col}\" = '{code}'",
+                )
+            )
+
+    # IAMC token dimensions via array containment (one block per resolved token)
+    for dim_key in IAMC_DIMENSIONS:
+        d = dimension(dim_key, reg)
+        for v in d["values"]:
+            if not v["iri"]:
+                continue  # unresolved token (e.g. ldv, additional) -> skip
+            token = v["code"].replace("'", "''")  # SQL-escape
+            # Exact-segment match, parser-friendly: wrap the string in delimiters
+            # and LIKE for "| <token> |". Avoids substring collisions (bev vs fcev)
+            # and ontop/JSQLParser issues with ANY(string_to_array(...)).
+            out.append(
+                _block(
+                    f"{TABLE}_{dim_key}_{_slug(v['code'])}",
+                    f'{subj} {d["predicate"]} {v["iri"]} .',
+                    f'SELECT "{PK}" FROM "{SCHEMA}"."{TABLE}" '
+                    f"WHERE ('{DELIM}' || \"{IAMC_COL}\" || '{DELIM}') "
+                    f"LIKE '%{DELIM}{token}{DELIM}%'",
+                )
+            )
+
+    return "\n".join(out)
+
+
+if __name__ == "__main__":
+    print(generate())
diff --git a/oekg/registry/loader.py b/oekg/registry/loader.py
new file mode 100644
index 000000000..a7c4c8a47
--- /dev/null
+++ b/oekg/registry/loader.py
@@ -0,0 +1,146 @@
+"""
+SPDX-FileCopyrightText: 2026 Jonas Huber <https://github.com/jh-RLI> © Reiner Lemoine Institut
+SPDX-License-Identifier: AGPL-3.0-or-later
+"""  # noqa: 501
+
+# Load the Dimension Property Registry and project it to the `registry.json`
+# CONTRACT. This is the single API for consuming the registry. Used by:
+#   * the OEP Django app  -> serves the contract as registry.json to the frontend
+#     (oekg/views.py: dimension_registry_view).
+#   * Philipp's mapping generator (imported by OEP as a Python package) -> OEP
+#     calls the generator entrypoint with this contract dict. The generator never
+#     imports OEP internals; it just accepts the dict.
+#
+# The contract is the ONE shared shape (frontend + generator + any external tool):
+#   {
+#     "version", "namespaces", "row_anchor", "generic_super_property",
+#     "dimensions": [
+#       {"key", "concept", "predicate", "object_kind", "datatype",
+#        "value_space", "values": [{"code", "iri", "label"}]}
+#     ]
+#   }
+# Value spaces are inlined per dimension as `values` (resolved code -> IRI + label)
+# so both the frontend (build filters) and the generator (emit one block per value)
+# read the same place. iamc_tokens are filtered to the dimension they belong to.
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import yaml
+
+REGISTRY_PATH = Path(__file__).with_name("dimension_property_registry.yaml")
+
+
+def _load_yaml(path: Path = REGISTRY_PATH) -> dict:
+    return yaml.safe_load(path.read_text(encoding="utf-8"))
+
+
+def _normalize_values(value_space: dict, dimension_key: str) -> list[dict]:
+    """Return [{code, iri, label}] for one dimension's value space.
+
+    Handles both shapes: flat ``code -> iri`` (gas/scenario/region) and
+    structured ``code -> {iri, dimension, name}`` (iamc_tokens). For the
+    structured shape, only values whose ``dimension`` matches are returned.
+    """
+    out: list[dict] = []
+    if not isinstance(value_space, dict):
+        return out
+    for code, val in value_space.items():
+        # skip non-string keys (YAML may coerce e.g. bare NO) and meta keys
+        if not isinstance(code, str) or code.startswith("_"):
+            continue
+        if isinstance(val, dict):
+            if val.get("dimension") and val.get("dimension") != dimension_key:
+                continue
+            out.append({"code": code, "iri": val.get("iri"), "label": val.get("name")})
+        else:
+            out.append({"code": code, "iri": val, "label": None})
+    return out
+
+
+def build_contract(raw: dict | None = None) -> dict:
+    """Project the raw registry YAML into the shared registry.json contract."""
+    raw = raw if raw is not None else _load_yaml()
+    value_spaces = raw.get("value_spaces", {})
+    dimensions = []
+    for d in raw.get("dimensions", []):
+        vs_name = d.get("value_space")
+        values = (
+            _normalize_values(value_spaces.get(vs_name, {}), d["key"])
+            if vs_name
+            else []
+        )
+        dimensions.append(
+            {
+                "key": d["key"],
+                "concept": d.get("concept"),
+                "predicate": d["predicate"],
+                "object_kind": d["object_kind"],
+                "datatype": d.get("datatype"),
+                "value_space": vs_name,
+                "values": values,
+            }
+        )
+    return {
+        "version": raw.get("version"),
+        "namespaces": raw.get("namespaces", {}),
+        "row_anchor": raw.get("row_anchor", {}),
+        "generic_super_property": raw.get("generic_super_property"),
+        "dimensions": dimensions,
+    }
+
+
+def load_registry() -> dict:
+    """The contract dict served by the Django view + consumed by the generator.
+
+    Reads the YAML fresh each call so registry edits appear without a restart
+    (the file is tiny; this endpoint is low-traffic).
+    """
+    return build_contract()
+
+
+# --- convenience accessors (handy for the generator) -------------------------
+def dimension(key: str, contract: dict | None = None) -> dict | None:
+    contract = contract or load_registry()
+    return next((d for d in contract["dimensions"] if d["key"] == key), None)
+
+
+def predicate_for(key: str, contract: dict | None = None) -> str | None:
+    d = dimension(key, contract)
+    return d["predicate"] if d else None
+
+
+def dimension_for_concept(concept: str, contract: dict | None = None) -> dict | None:
+    """Find the dimension whose ``concept`` (isAbout class IRI) matches.
+
+    This is the JOIN KEY the generator uses to bind a table column (its
+    oemetadata ``isAbout``) to a registry dimension, and thus its predicate.
+    Returns None if unmapped; raises if >1 dimension claims the concept
+    (concepts must be unique — see validate_registry.py).
+
+    NB: a generic concept shared across IAMC dimensions (e.g. "variable") is NOT
+    bound this way — those columns decompose via the token dictionary instead.
+    """
+    contract = contract or load_registry()
+    hits = [d for d in contract["dimensions"] if d.get("concept") == concept]
+    if len(hits) > 1:
+        raise ValueError(
+            f"ambiguous concept {concept!r}: claimed by {[d['key'] for d in hits]}"
+        )
+    return hits[0] if hits else None
+
+
+def expand(curie: str, contract: dict | None = None) -> str:
+    """Expand a ``prefix:Local`` CURIE to a full IRI using the registry prefixes."""
+    contract = contract or load_registry()
+    if ":" not in curie:
+        return curie
+    prefix, local = curie.split(":", 1)
+    base = contract["namespaces"].get(prefix)
+    return f"{base}{local}" if base else curie
+
+
+if __name__ == "__main__":
+    print(json.dumps(build_contract(), indent=2, ensure_ascii=False))
diff --git a/oekg/registry/registry.linkml.sample.yaml b/oekg/registry/registry.linkml.sample.yaml
new file mode 100644
index 000000000..1b9a6ae08
--- /dev/null
+++ b/oekg/registry/registry.linkml.sample.yaml
@@ -0,0 +1,91 @@
+# =============================================================================
+# SAMPLE — Dimension Property Registry expressed as a LinkML schema
+# -----------------------------------------------------------------------------
+# A 3-dimension excerpt to evaluate LinkML as the registry format (Philipp's
+# mapping generator already uses LinkML). Shows how the registry's two core
+# concepts map onto NATIVE LinkML constructs:
+#   * dimension -> predicate    == a slot's `slot_uri`
+#   * code -> ontology IRI      == an enum `permissible_values[...].meaning`
+#   * object_kind / datatype    == the slot's `range` (enum => IRI; double/int => literal)
+#   * generic `is about` + typed refinement == slot `is_a` hierarchy (=> rdfs:subPropertyOf)
+#
+# From this ONE file, LinkML generates what each consumer needs:
+#   gen-owl  -> RDF/OWL (KG bridge)   gen-shacl -> validation shapes
+#   gen-json-schema -> UI / validation   gen-pydantic -> classes for the generator
+#
+# NOT the source of truth yet — the live registry is dimension_property_registry.yaml.
+# =============================================================================
+id: https://openenergyplatform.org/oekg/scenario-comparison-registry
+name: scenario_comparison_registry
+title: Scenario Comparison Dimension Property Registry (sample)
+description: >-
+  Harmonized dimensions for cross-dataset scenario comparison. Each slot's
+  slot_uri is the predicate; enums carry code -> ontology IRI via `meaning`.
+license: AGPL-3.0
+default_range: string
+default_prefix: oekg
+imports:
+  - linkml:types
+
+prefixes:
+  linkml: https://w3id.org/linkml/
+  oekg: https://openenergyplatform.org/ontology/oeo/oekg/
+  oeo: https://openenergyplatform.org/ontology/oeo/ # base for ALL terms (OEO_*, IAO_*, UO_*)
+  llc: https://www.omg.org/spec/LCC/Countries/ISO3166-1-CountryCodes/
+
+# -----------------------------------------------------------------------------
+classes:
+  ScenarioObservation:
+    description: One harmonized data point (maps to one source table row).
+    class_uri: oeo:IAO_0000027 # data item  == registry row_anchor.class
+    tree_root: true
+    slots:
+      - spatial_region # controlled-vocab dimension (IRI object)
+      - quantity_value # measurement (literal)
+      - technology # IAMC token dimension (IRI object, interim is-about)
+
+# -----------------------------------------------------------------------------
+slots:
+  # Abstract generic relation. Specific dimension slots are `is_a` this, so
+  # gen-owl emits their predicate as rdfs:subPropertyOf `is about`.
+  is_about:
+    abstract: true
+    slot_uri: oeo:IAO_0000136 # "is about"
+    description:
+      Generic annotation relation; super-property of the typed dimensions.
+
+  spatial_region:
+    slot_uri: oeo:OEO_00020221 # "has considered region"  (dimension -> predicate)
+    range: RegionEnum # enum range => object is an IRI
+    multivalued: false
+
+  quantity_value:
+    slot_uri: oeo:OEO_00140178 # quantity/emission value
+    range: double # primitive range => literal object (xsd:double)
+
+  technology:
+    is_a: is_about # refine later: set slot_uri to oeo:hasTechnology
+    slot_uri: oeo:IAO_0000136 # INTERIM: generic "is about" (decision 2026-06-12)
+    range: TechnologyEnum
+
+# -----------------------------------------------------------------------------
+enums:
+  RegionEnum:
+    description: ISO-3166-1 alpha-2 code -> LCC country IRI.
+    permissible_values:
+      AT: { meaning: "llc:Austria" }
+      DE: { meaning: "llc:Germany" }
+      "NO":
+        {
+          meaning: "llc:Norway",
+          description: "quoted: bare NO is YAML boolean",
+        }
+
+  TechnologyEnum:
+    description: IAMC powertrain tokens -> OEO IRIs (TS-resolved).
+    permissible_values:
+      bev: { meaning: "oeo:OEO_00010024", title: battery electric vehicle }
+      fcev: { meaning: "oeo:OEO_00010025", title: fuel cell electric vehicle }
+      ice: { meaning: "oeo:OEO_00000240", title: internal combustion vehicle }
+      phev:
+        { meaning: "oeo:OEO_00010030", title: plug-in hybrid electric vehicle }
diff --git a/oekg/registry/resolve_terms.py b/oekg/registry/resolve_terms.py
new file mode 100644
index 000000000..b4d8f18a0
--- /dev/null
+++ b/oekg/registry/resolve_terms.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""Resolve human labels to ontology IRIs via the TIB Terminology Service.
+
+This is the "use the TS instead of hand-maintaining term IRIs" tool. It turns a
+label (e.g. "battery electric vehicle") into a canonical OEO IRI by querying
+https://api.terminology.tib.eu, preferring an exact label match and flagging
+ambiguous results for human confirmation.
+
+Why human-in-the-loop: short codes are ambiguous (e.g. "CO2" returns carbon
+dioxide *and* "CO2 price", "CO2 emission", …; "investment" vs "investment cost").
+Exact-label matches are auto-confident; everything else is reported, not assumed.
+
+Note on namespaces: OEO imports BFO/IAO/UO/etc. Imported terms keep their obo
+IRI (obo/IAO_0000136, obo/UO_0000000) and their imported label; only OEO-native
+terms use the oeo: IRI. So results may legitimately mix namespaces. Searching
+with ontology=oeo may not surface imported terms -- drop the filter, or use
+/api/terms/findByIdAndIsDefiningOntology to get the defining-ontology IRI.
+
+Usage:
+    # resolve one or more labels
+    python oekg/registry/resolve_terms.py "battery electric vehicle" "energy demand"
+
+    # resolve every still-null token name in the registry's iamc_tokens and
+    # print a paste-ready report (does NOT rewrite the commented YAML)
+    python oekg/registry/resolve_terms.py --fill-iamc-tokens
+
+Requires network access and the `requests` package.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+from urllib.parse import quote
+
+API = "https://api.terminology.tib.eu/api/search"
+DEFAULT_ONTOLOGY = "oeo"
+CANONICAL_PREFIX = "https://openenergyplatform.org/ontology/oeo/"
+REGISTRY = Path(__file__).with_name("dimension_property_registry.yaml")
+
+
+def _get_json(url: str) -> dict:
+    import requests  # imported late so --help works without the dep
+
+    resp = requests.get(url, headers={"Accept": "application/json"}, timeout=20)
+    resp.raise_for_status()
+    return resp.json()
+
+
+def search(
+    label: str,
+    ontology: str = DEFAULT_ONTOLOGY,
+    want_type: str | None = None,
+    rows: int = 10,
+) -> list[dict]:
+    """Return candidate terms [{iri, label, ontology, type}] for a label.
+
+    NB: do NOT default to type=class — OEO holds some concepts as *individuals*
+    (e.g. CRF sectors: "CRF sector (IPCC 2006): domestic aviation"). Omitting the
+    type filter returns classes AND individuals. Pass want_type only to narrow.
+    """
+    url = f"{API}?q={quote(label)}&ontology={quote(ontology)}&rows={rows}"
+    if want_type:
+        url += f"&type={quote(want_type)}"
+    docs = _get_json(url).get("response", {}).get("docs", [])
+    return [
+        {
+            "iri": d.get("iri"),
+            "label": d.get("label"),
+            "ontology": d.get("ontology_name"),
+            "type": d.get("type"),
+        }
+        for d in docs
+    ]
+
+
+def resolve(label: str, **kw) -> dict:
+    """Resolve a label to a single best result with a confidence verdict.
+
+    Returns {label, iri, match, candidates} where match is one of:
+      'exact'     — exactly one case-insensitive label match (auto-confident)
+      'ambiguous' — several candidates / no exact label match (confirm manually)
+      'none'      — nothing came back
+    """
+    cands = search(label, **kw)
+    if not cands:
+        return {"label": label, "iri": None, "match": "none", "candidates": []}
+    want = label.strip().lower()
+    exact = [c for c in cands if (c["label"] or "").strip().lower() == want]
+    if len(exact) == 1:
+        return {
+            "label": label,
+            "iri": exact[0]["iri"],
+            "match": "exact",
+            "candidates": cands,
+        }
+    return {"label": label, "iri": None, "match": "ambiguous", "candidates": cands}
+
+
+def _short(iri: str | None) -> str:
+    if not iri:
+        return "null"
+    if iri.startswith(CANONICAL_PREFIX):
+        return "oeo:" + iri[len(CANONICAL_PREFIX) :]
+    return iri
+
+
+def _report(results: list[dict]) -> int:
+    unresolved = 0
+    for r in results:
+        if r["match"] == "exact":
+            print(f"  ✅ {r['label']!r:45} -> {_short(r['iri'])}")
+        else:
+            unresolved += 1
+            tag = "❓ ambiguous" if r["match"] == "ambiguous" else "∅ not found"
+            print(f"  {tag}: {r['label']!r}")
+            for c in r["candidates"][:5]:
+                print(f"       - {_short(c['iri'])}  ({c['label']})")
+    print(
+        f"\n{len(results) - unresolved}/{len(results)} auto-resolved; "
+        f"{unresolved} need manual confirmation."
+    )
+    return unresolved
+
+
+def fill_iamc_tokens() -> int:
+    import yaml
+
+    data = yaml.safe_load(REGISTRY.read_text(encoding="utf-8"))
+    tokens = data["value_spaces"]["iamc_tokens"]
+    todo = {
+        k: v for k, v in tokens.items() if isinstance(v, dict) and v.get("iri") is None
+    }
+    if not todo:
+        print("All iamc_tokens already have IRIs. 🎉")
+        return 0
+    print(f"Resolving {len(todo)} unresolved token name(s) against the TS…\n")
+    results = [resolve(v["name"]) for v in todo.values()]
+    rc = _report(results)
+    print(
+        "\nPaste the ✅ lines into dimension_property_registry.yaml "
+        "(iamc_tokens), tag them [ts]. Confirm the ❓ ones first."
+    )
+    return rc
+
+
+def main(argv: list[str]) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("labels", nargs="*", help="labels to resolve")
+    ap.add_argument("--ontology", default=DEFAULT_ONTOLOGY)
+    ap.add_argument("--type", default="class", dest="want_type")
+    ap.add_argument(
+        "--fill-iamc-tokens",
+        action="store_true",
+        help="resolve every null token name in the registry",
+    )
+    args = ap.parse_args(argv)
+
+    if args.fill_iamc_tokens:
+        return 1 if fill_iamc_tokens() else 0
+    if not args.labels:
+        ap.print_help()
+        return 0
+    results = [
+        resolve(lbl, ontology=args.ontology, want_type=args.want_type)
+        for lbl in args.labels
+    ]
+    return 1 if _report(results) else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1:]))
diff --git a/oekg/registry/validate_registry.py b/oekg/registry/validate_registry.py
new file mode 100644
index 000000000..c39d1aa38
--- /dev/null
+++ b/oekg/registry/validate_registry.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+"""Validate the Dimension Property Registry and report its completion state.
+
+Standalone (no Django imports) so it can run in CI or be vendored by the
+separate mapping-generator repo. It checks structural integrity and prints
+what is still [verify]/[proposed]/TODO — i.e. the Phase-1 work list.
+
+Usage:
+    python oekg/registry/validate_registry.py
+Exit code 0 if structurally valid (TODOs are warnings, not errors).
+Exit code 1 on a structural error (missing keys, dangling value_space ref).
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import yaml
+
+REGISTRY = Path(__file__).with_name("dimension_property_registry.yaml")
+
+
+def main() -> int:
+    data = yaml.safe_load(REGISTRY.read_text(encoding="utf-8"))
+    errors: list[str] = []
+    todos: list[str] = []
+
+    # --- structural checks -------------------------------------------------
+    for key in ("version", "namespaces", "row_anchor", "dimensions", "value_spaces"):
+        if key not in data:
+            errors.append(f"missing top-level key: {key}")
+    if errors:
+        _print_errors(errors)
+        return 1
+
+    value_spaces = data["value_spaces"]
+    seen_keys: set[str] = set()
+
+    for dim in data["dimensions"]:
+        key = dim.get("key", "<no key>")
+        if key in seen_keys:
+            errors.append(f"duplicate dimension key: {key}")
+        seen_keys.add(key)
+
+        # every dimension needs a predicate
+        pred = dim.get("predicate")
+        if not pred:
+            errors.append(f"[{key}] has no predicate")
+        elif str(pred).startswith("TODO"):
+            todos.append(f"[{key}] predicate not yet minted: {pred}")
+
+        # object_kind sanity
+        ok = dim.get("object_kind")
+        if ok not in ("iri", "literal"):
+            errors.append(f"[{key}] object_kind must be 'iri' or 'literal', got {ok!r}")
+        if ok == "literal" and not dim.get("datatype"):
+            errors.append(f"[{key}] literal dimension needs a datatype")
+
+        # value_space must resolve
+        vs = dim.get("value_space")
+        if ok == "iri" and not vs:
+            errors.append(f"[{key}] iri dimension needs a value_space")
+        if vs and vs not in value_spaces:
+            errors.append(f"[{key}] dangling value_space reference: {vs}")
+
+        # Token-bound (IAMC) dimensions bind via the token dictionary, and literal
+        # dimensions (measures / dataset labels) bind by column, not via an
+        # isAbout concept — a null concept is fine for them, not a TODO.
+        token_bound = dim.get("value_space") == "iamc_tokens"
+        is_literal = dim.get("object_kind") == "literal"
+        if dim.get("concept") is None and not token_bound and not is_literal:
+            todos.append(f"[{key}] concept (isAbout class) not confirmed")
+
+    # --- concept uniqueness (the join key to oemetadata isAbout) -----------
+    # A column's isAbout IRI binds it to the dimension with the matching concept.
+    # If two dimensions share a concept, that binding is ambiguous.
+    concept_seen: dict[str, str] = {}
+    for dim in data["dimensions"]:
+        c = dim.get("concept")
+        if not c:
+            continue
+        if c in concept_seen:
+            errors.append(
+                f"concept {c} shared by [{concept_seen[c]}] and [{dim.get('key')}] "
+                f"— must be unique (it is the oemetadata isAbout join key)"
+            )
+        else:
+            concept_seen[c] = dim.get("key")
+
+    # --- value-space IRI completeness (correctness gate 2) -----------------
+    for vs_name, mapping in value_spaces.items():
+        if not isinstance(mapping, dict):
+            continue
+        for code, val in mapping.items():
+            # YAML may coerce bare keys (NO/YES/ON/OFF -> bool); flag, don't crash
+            if not isinstance(code, str):
+                errors.append(
+                    f"value_space[{vs_name}] has non-string key {code!r} "
+                    f'(quote it in YAML, e.g. "NO")'
+                )
+                continue
+            if code.startswith("_"):  # convention/meta keys
+                continue
+            iri = val.get("iri") if isinstance(val, dict) else val
+            if iri is None:
+                todos.append(f"value_space[{vs_name}][{code}] has no IRI (@id null)")
+
+    # --- report ------------------------------------------------------------
+    if errors:
+        _print_errors(errors)
+        return 1
+
+    print(
+        f"✅ Registry v{data['version']} is structurally valid "
+        f"({len(data['dimensions'])} dimensions, {len(value_spaces)} value spaces)."
+    )
+    if todos:
+        print(f"\n⚠️  {len(todos)} item(s) still need attention (Phase-1 work list):")
+        for t in todos:
+            print(f"   - {t}")
+    else:
+        print("🎉 No TODOs — registry is fully populated.")
+    return 0
+
+
+def _print_errors(errors: list[str]) -> None:
+    print("❌ Registry is INVALID:")
+    for e in errors:
+        print(f"   - {e}")
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/oekg/urls.py b/oekg/urls.py
index bcd02b2eb..b31581f0e 100644
--- a/oekg/urls.py
+++ b/oekg/urls.py
@@ -7,6 +7,7 @@
 from django.urls import path
 
 from oekg.views import (
+    dimension_registry_view,
     filter_oekg_by_scenario_bundles_attributes_view,
     main_view,
     sparql_endpoint_view,
@@ -18,6 +19,7 @@
     path("gui/", main_view, name="main"),
     path("sparql/", sparql_endpoint_view, name="sparql_endpoint"),
     path("sparql_info/", sparql_metadata_view, name="sparql_endpoint_info"),
+    path("registry/", dimension_registry_view, name="dimension_registry"),
     path(
         "filter-by-criteria/",
         filter_oekg_by_scenario_bundles_attributes_view,
diff --git a/oekg/views.py b/oekg/views.py
index 6e6796d29..ec5a36cf8 100644
--- a/oekg/views.py
+++ b/oekg/views.py
@@ -13,6 +13,7 @@
 from django.shortcuts import render
 from django.views.decorators.http import require_GET, require_POST
 
+from oekg.registry.loader import load_registry
 from oekg.utils import execute_filter_sparql_query, execute_sparql_query
 from oeplatform.settings import DOCUMENTATION_LINKS
 
@@ -26,6 +27,17 @@ def main_view(request):
     return response
 
 
+@require_GET
+def dimension_registry_view(request):
+    """Serve the Dimension Property Registry contract (registry.json).
+
+    The single shared vocabulary consumed by the comparison frontend (to build
+    dynamic SPARQL + filter UI) and by the mapping generator (passed as a dict).
+    Source of truth: oekg/registry/dimension_property_registry.yaml.
+    """
+    return JsonResponse(load_registry(), safe=False)
+
+
 @require_POST
 def sparql_endpoint_view(request):
     """