Triple
T22474430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alain Chabat |
E555589
|
entity |
| Predicate | awardReceivedFor |
P107
|
FINISHED |
| Object | Didier |
—
|
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: Didier | Statement: [Alain Chabat, awardReceivedFor, Didier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Didier Context triple: [Alain Chabat, awardReceivedFor, Didier]
-
A.
Didier
Didier is a masculine given name of French origin, notably borne by Ivorian football legend Didier Drogba.
-
B.
Didier
chosen
Didier is a French comedy film written, directed by, and starring Alain Chabat, in which a man is unexpectedly transformed into a dog-like human.
-
C.
Thierry
Thierry is a French given name most famously borne by legendary footballer Thierry Henry.
-
D.
Didier Hoarau
Didier Hoarau is a film producer known for his work on the action thriller movie "Taken."
-
E.
François de Donadieu
François de Donadieu was a French Roman Catholic prelate who served as bishop in the Diocese of Auxerre during the late 16th and early 17th centuries.
- 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_69e11e52c2048190952dc5df209b9bed |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15be2d5388190a59d11b3403d998b |
completed | April 29, 2026, 1:16 a.m. |
Created at: April 16, 2026, 8:49 p.m.