Triple
T5933583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carrefour |
E131992
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | Carrefour Bio |
E131992
|
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: Carrefour Bio | Statement: [Carrefour, hasBrand, Carrefour Bio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carrefour Bio Context triple: [Carrefour, hasBrand, Carrefour Bio]
-
A.
Carrefour
chosen
Carrefour is a major French multinational retail corporation and one of the world’s largest hypermarket chains.
-
B.
Carrefour
Carrefour is a major suburban city in Haiti, located just southwest of the capital Port-au-Prince and known as part of the country's largest metropolitan area.
-
C.
Danone
Danone is a multinational French food-products corporation best known for its dairy, plant-based, and bottled water brands.
-
D.
Saputo Inc.
Saputo Inc. is a major Canadian dairy company that produces and distributes a wide range of cheese and other dairy products internationally.
-
E.
Arcabonne
Arcabonne is a sorceress and antagonist from the medieval chivalric romance cycle of *Amadis*, known for her vengeful and magical opposition to the hero.
- 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_69c0c064d2a4819096085668182cfde1 |
completed | March 23, 2026, 4:24 a.m. |
Created at: March 22, 2026, 4 p.m.