Triple
T4315139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puy-de-Dôme (department) |
E94169
|
entity |
| Predicate | majorCompany |
P597
|
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: [Puy-de-Dôme (department), majorCompany, Michelin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelin Context triple: [Puy-de-Dôme (department), majorCompany, 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.
Bridgestone
Bridgestone is a global tire and rubber company headquartered in Japan, known for its extensive involvement in motorsports and major sports sponsorships.
-
D.
Dunlop
Dunlop is a well-known tire manufacturer that produces high-performance motorcycle and automotive tires used in both professional racing and everyday driving.
-
E.
Continental
Continental is a major German automotive manufacturing company best known for producing tires, braking systems, and other vehicle components.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350f4448481908be2c7df9cc71bb9 |
completed | March 12, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d08245408190b1ce584c636bf168 |
completed | March 14, 2026, 9:17 p.m. |
Created at: March 12, 2026, 11:12 p.m.