Triple
T37729217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bertrand Morane |
E940109
|
entity |
| Predicate | remakeFilm |
P188290
|
FINISHED |
| Object | The Man Who Loved Women (1983 American film) |
—
|
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: The Man Who Loved Women (1983 American film) | Statement: [Bertrand Morane, remakeFilm, The Man Who Loved Women (1983 American film)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: remakeFilm Context triple: [Bertrand Morane, remakeFilm, The Man Who Loved Women (1983 American film)]
-
A.
remakeFeature
Indicates that one work is a new version or adaptation created by redoing or updating the features of an earlier work.
-
B.
filmRemakeOf
chosen
Indicates that one film is a remake of another earlier film, typically retelling the same or a very similar story.
-
C.
remakeOfFilmBy
Indicates that one film is a remake created by a specific filmmaker or director of an earlier film.
-
D.
remadeFor
Indicates that one work has been recreated or adapted specifically for another context, audience, or medium.
-
E.
remakeOfFilmStarring
Indicates that one film is a remake of another film in which a specified actor or set of actors starred.
- 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_69f76edefd048190a32212c5c3919531 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb9e8108c8190ae1c7940b1677e95 |
completed | May 6, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fbb141605c8190b9c27d70352522db |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:18 p.m.