add dataset identifier

docs/challenge.md

@@ -26,6 +26,8 @@ As one of the most comprehensive and widely used knowledge graphs, DBpedia repre
 It contains a vast array of topics, relationships, and entities, providing a robust testing ground for models designed to handle complex, diverse, and large knowledge graphs.
 Evaluating on DBpedia will measure the ability of models to scale effectively and manage queries involving extensive datasets.
 
+The identifier for this dataset is: `https://text2sparql.aksw.org/2025/dbpedia/`
+
 ??? example "Dumps"
 
     - [dbpedia_2015-10.nt](https://downloads.dbpedia.org/2015-10/dbpedia_2015-10.nt)
@@ -62,6 +64,8 @@ This evaluation highlights performance in scenarios where precision, domain rele
 By evaluating models separately on these two knowledge graphs, the challenge ensures a comprehensive assessment of scalability, adaptability, and domain-specific reasoning.
 This dual evaluation also provides insights into the generalizability of models across knowledge graph sizes and complexities, reflecting real-world applications in both open-domain and specialized environments.
 
+The identifier for this dataset is: `https://text2sparql.aksw.org/2025/corporate/`
+
 ??? example "Dumps"
 
     - [prod-vocab.ttl](data/prod-vocab.ttl) - Ontology (13 classes, 30 properties)