Triple

T8304695
Position Surface form Disambiguated ID Type / Status
Subject Racing 92 E194433 entity
Predicate formerName P65 FINISHED
Object Racing Métro 92
Racing Métro 92 was the former name of the professional French rugby union club now known as Racing 92, based in the Paris region and competing in the Top 14.
 E725397 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: Racing Métro 92 | Statement: [Racing 92, formerName, Racing Métro 92]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Racing Métro 92
Context triple: [Racing 92, formerName, Racing Métro 92]
  • A. Le Racing
    Le Racing is the popular nickname of RC Strasbourg Alsace, a historic French football club based in Strasbourg.
  • B. Le Mans FC
    Le Mans FC is a French professional football club based in the city of Le Mans, known for competing in the French football league system and playing its home matches at the MMArena.
  • C. TGV Paris–Nice
    TGV Paris–Nice is a high-speed French train service connecting Paris with the Mediterranean city of Nice.
  • D. Paris–Toulouse
    Paris–Toulouse is a major intercity rail corridor in France linking the capital Paris with the southwestern city of Toulouse.
  • E. Le Mans
    Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
  • 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: Racing Métro 92
Triple: [Racing 92, formerName, Racing Métro 92]
Generated description
Racing Métro 92 was the former name of the professional French rugby union club now known as Racing 92, based in the Paris region and competing in the Top 14.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Racing Métro 92
Target entity description: Racing Métro 92 was the former name of the professional French rugby union club now known as Racing 92, based in the Paris region and competing in the Top 14.
  • A. Le Racing
    Le Racing is the popular nickname of RC Strasbourg Alsace, a historic French football club based in Strasbourg.
  • B. Le Mans FC
    Le Mans FC is a French professional football club based in the city of Le Mans, known for competing in the French football league system and playing its home matches at the MMArena.
  • C. TGV Paris–Nice
    TGV Paris–Nice is a high-speed French train service connecting Paris with the Mediterranean city of Nice.
  • D. Paris–Toulouse
    Paris–Toulouse is a major intercity rail corridor in France linking the capital Paris with the southwestern city of Toulouse.
  • E. Le Mans
    Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
  • 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_69ca82e613e88190bf8139669bbd0d53 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7e8db3a8819083772db5c7a2454b completed March 31, 2026, 7:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd9545ffc48190869906b02692b873 completed April 1, 2026, 9:59 p.m.
NEDg Description generation batch_69cdab5d649c819098a7643d5a0b7827 completed April 1, 2026, 11:33 p.m.
NED2 Entity disambiguation (via description) batch_69cdb2c2e2248190bf52466abaebfe29 completed April 2, 2026, 12:05 a.m.
Created at: March 30, 2026, 5:54 p.m.