Triple
T5934067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bouygues |
E132001
|
entity |
| Predicate | owns |
P347
|
FINISHED |
| Object | Colas |
E556352
|
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: Colas | Statement: [Bouygues, owns, Colas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colas Context triple: [Bouygues, owns, Colas]
-
A.
Colas
chosen
Colas is a major French civil engineering and construction company best known for its global road-building and transport infrastructure activities.
-
B.
Benissanet
Benissanet is a small municipality in Catalonia, Spain, situated along the Ebro River and known for its agricultural landscape and historic village core.
-
C.
Bougros
Bougros is one of the prestigious Grand Cru vineyard sites in the Chablis wine region of Burgundy, France, known for producing high-quality Chardonnay wines.
-
D.
Colla
The Colla are an indigenous Aymara-speaking people of the Andean highlands, historically inhabiting the region that formed part of the Inca Empire’s Collasuyu.
-
E.
Cellese
Cellese is a regional dialect of the Franco-Provençal language traditionally spoken in a specific area of the Franco-Provençal linguistic region.
- 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_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0389f6fc881909527b928838ffcdd |
completed | March 22, 2026, 6:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e3affd748190a37e3cc60e58d6a6 |
completed | March 23, 2026, 6:54 a.m. |
Created at: March 22, 2026, 4 p.m.