Triple
T15921897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Édouard Michelin |
E386111
|
entity |
| Predicate | coFounded |
P104
|
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: [Édouard Michelin, coFounded, Michelin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelin Context triple: [Édouard Michelin, coFounded, 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 (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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156825b1881908477ec93cc7b5f02 |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe46b06688190a02fee3700efd709 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:52 a.m.