Triple
T8626531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | André Bazin |
E204292
|
entity |
| Predicate | activeYearsInFilmCriticism |
P15974
|
FINISHED |
| Object | 1940s–1958 |
—
|
LITERAL 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: 1940s–1958 | Statement: [André Bazin, activeYearsInFilmCriticism, 1940s–1958]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInFilmCriticism Context triple: [André Bazin, activeYearsInFilmCriticism, 1940s–1958]
-
A.
activeYearsInFilm
chosen
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
B.
activeYearsInLiterature
Indicates the span of years during which an entity was actively producing or contributing to literary works.
-
C.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
D.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
E.
yearOfFilmAppearance
Indicates the specific year in which a film appearance by an entity took place.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5730309081909a9a0256c9bf5f8f |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc455906f8819082edd79cb4a1cf28 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:26 p.m.