Triple

T13487717
Position Surface form Disambiguated ID Type / Status
Subject Stade de Gerland E318548 entity
Predicate formerTenant P7727 FINISHED
Object Lyon OU
Lyon OU is a French rugby union club based in Lyon that competes in the country’s top professional league.
E1043613 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lyon OU | Statement: [Stade de Gerland, formerTenant, Lyon OU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lyon OU
Context triple: [Stade de Gerland, formerTenant, Lyon OU]
  • A. CPE Lyon
    CPE Lyon is a French grande école of engineering and chemistry located in the Lyon metropolitan area.
  • B. Les Lyonnais
    Les Lyonnais is a French film in which actress Corinne Marchand delivered one of her most recognized performances.
  • C. Lyonnet
    Lyonnet is a French surname most notably associated with professional dancer Grégoire Lyonnet.
  • D. MHC Lyons
    MHC Lyons is an Irish architectural firm known for its contemporary designs and contributions to modern urban development.
  • E. Clermont
    Clermont is a rural town in Central Queensland, Australia, known historically for gold and coal mining and as a service centre for surrounding agricultural and mining regions.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lyon OU
Triple: [Stade de Gerland, formerTenant, Lyon OU]
Generated description
Lyon OU is a French rugby union club based in Lyon that competes in the country’s top professional league.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lyon OU
Target entity description: Lyon OU is a French rugby union club based in Lyon that competes in the country’s top professional league.
  • A. CPE Lyon
    CPE Lyon is a French grande école of engineering and chemistry located in the Lyon metropolitan area.
  • B. Les Lyonnais
    Les Lyonnais is a French film in which actress Corinne Marchand delivered one of her most recognized performances.
  • C. Lyonnet
    Lyonnet is a French surname most notably associated with professional dancer Grégoire Lyonnet.
  • D. MHC Lyons
    MHC Lyons is an Irish architectural firm known for its contemporary designs and contributions to modern urban development.
  • E. Clermont
    Clermont is a rural town in Central Queensland, Australia, known historically for gold and coal mining and as a service centre for surrounding agricultural and mining regions.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf3b9b488190bb4e11424ff599c8 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f74638e2088190a126791f60b541c7 completed May 3, 2026, 12:57 p.m.
NEDg Description generation batch_69f74cb6cae881909563983a38311db7 completed May 3, 2026, 1:25 p.m.
NED2 Entity disambiguation (via description) batch_69f74d5389f08190826f0e550c8bb6c2 completed May 3, 2026, 1:27 p.m.
Created at: April 9, 2026, 9:42 p.m.