Triple

T1179104
Position Surface form Disambiguated ID Type / Status
Subject Clermont-Ferrand E25094 entity
Predicate headquartersOf P62 FINISHED
Object Michelin E131996 NE FINISHED

How this triple was built (2 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: Michelin | Statement: [Clermont-Ferrand, headquartersOf, Michelin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michelin
Context triple: [Clermont-Ferrand, headquartersOf, Michelin]
  • A. Michelin chosen
    Michelin is a major French multinational tire manufacturer renowned for its tires, travel guides, and the Michelin star restaurant rating system.
  • B. Bridgestone
    Bridgestone is a global tire and rubber company headquartered in Japan, known for its extensive involvement in motorsports and major sports sponsorships.
  • C. Continental
    Continental is a major German automotive manufacturing company best known for producing tires, braking systems, and other vehicle components.
  • D. Goodyear Tire & Rubber Company
    Goodyear Tire & Rubber Company is a major American multinational manufacturer of tires and rubber products, best known for supplying automotive tires worldwide and for its iconic Goodyear Blimp.
  • E. Traton
    Traton is a commercial vehicle manufacturer and holding company that oversees brands like MAN and Scania within the Volkswagen Group.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a494267b4c819088c97a59182bf56a completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd1226fc819083d526ecd22af8ef completed March 1, 2026, 10:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac6f1f1c188190a96f5718c4e7d59d completed March 7, 2026, 6:31 p.m.
Created at: March 1, 2026, 7:45 p.m.