Triple

T22462783
Position Surface form Disambiguated ID Type / Status
Subject Michelin Travel Guides E555270 entity
Predicate brandOf P1500 FINISHED
Object Michelin NE NERFINISHED

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: [Michelin Travel Guides, brandOf, Michelin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michelin
Context triple: [Michelin Travel Guides, brandOf, 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. Pirelli
    Pirelli is an Italian multinational company best known as one of the world’s leading manufacturers of high-performance tyres, particularly in motorsport and premium road vehicles.
  • C. Goodyear
    Goodyear is a rapidly growing suburban city in the Phoenix metropolitan area of Arizona, known for its master-planned communities, spring training baseball facilities, and proximity to desert recreation.
  • D. Bridgestone
    Bridgestone is a global tire and rubber company headquartered in Japan, known for its extensive involvement in motorsports and major sports sponsorships.
  • E. Continental Motors
    Continental Motors is an American manufacturer best known for producing aircraft and military vehicle engines, including powerplants for tanks and other armored vehicles.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e51fdec8190adfdf9f8a6362221 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b80983081908084947b2e31c9d4 completed April 29, 2026, 1:14 a.m.
Created at: April 16, 2026, 8:48 p.m.