Triple
T22297951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cécile |
E551171
|
entity |
| Predicate | hasNameInOriginalLanguage |
P29322
|
FINISHED |
| Object | Cécile |
—
|
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: Cécile | Statement: [Cécile, hasNameInOriginalLanguage, Cécile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cécile Context triple: [Cécile, hasNameInOriginalLanguage, Cécile]
-
A.
Cécile
Cécile is the sensitive and central protagonist of the French film "Cible émouvante," around whom the story’s emotional and narrative developments revolve.
-
B.
Cécile
Cécile is a character in Jacques Demy’s 1961 French musical film "Lola."
-
C.
Cécile
Cécile is a French actress and filmmaker best known for her work in independent cinema and for being the daughter of actors Jean-Louis Trintignant and Nadine Marquand.
-
D.
Cécile
Cécile is the introspective, emotionally conflicted teenage protagonist of the French film "Bonjour Tristesse," whose relationships and moral choices drive the story’s drama.
-
E.
Clélia
Clélia is a feminine given name, primarily used in French-speaking countries and derived from the Latin name Clelia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e11e45fb848190a1b2ae21296e3a5f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1572200c88190b9413286136fef15 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.