Triple
T35399648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Repulsion (1965 film) |
E1023186
|
entity |
| Predicate | leadActorForCharacter Carole |
P183760
|
FINISHED |
| Object | Catherine Deneuve |
—
|
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: Catherine Deneuve | Statement: [Repulsion (1965 film), leadActorForCharacter Carole, Catherine Deneuve]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorForCharacter Carole Context triple: [Repulsion (1965 film), leadActorForCharacter Carole, Catherine Deneuve]
-
A.
leadActressCharacterName
Indicates the name of the character portrayed by the lead actress in a given work.
-
B.
leadActressPlaysCharacter
chosen
Indicates that a lead actress portrays or performs the role of a specific character.
-
C.
leadRoleActor
Indicates that an actor performs a leading or principal role in a work or production.
-
D.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
E.
leadCharacterCaste
Indicates that the lead character in a work belongs to a specified caste.
- F. None of above.
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_69f76df43ca4819098711ca4370f1bb9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:03 p.m.