Triple
T698750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinéfondation |
E13950
|
entity |
| Predicate | typicalNumberOfSelectedFilms |
P16787
|
FINISHED |
| Object | about 15 to 20 |
—
|
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: about 15 to 20 | Statement: [Cinéfondation, typicalNumberOfSelectedFilms, about 15 to 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfSelectedFilms Context triple: [Cinéfondation, typicalNumberOfSelectedFilms, about 15 to 20]
-
A.
typicalNumberOfNominees
Indicates the usual or standard count of nominees associated with something, such as an award, position, or selection process.
-
B.
maximumNominationsPerFilm
Indicates the highest number of nominations that any single film is allowed to receive.
-
C.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given entity.
-
D.
numberOfFilmsAppearedIn
Indicates the total count of distinct films in which a given entity has appeared.
-
E.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0dd4afc81909e4e869356006f33 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d2586b081908e052cc5ba1d2685 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a49dc20880819085fa60dc1851f9dc |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:36 p.m.