Triple
T14785622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentine cinema |
E347514
|
entity |
| Predicate | academyAwardBestForeignLanguageFilmWinCount |
P116098
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Argentine cinema, academyAwardBestForeignLanguageFilmWinCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academyAwardBestForeignLanguageFilmWinCount Context triple: [Argentine cinema, academyAwardBestForeignLanguageFilmWinCount, 2]
-
A.
bestForeignLanguageFilmWinner
Indicates that the subject is the film that won the award for Best Foreign Language Film in the specified context or event.
-
B.
bestForeignFilmHonoraryAwardCountry
Indicates the country that received an honorary award for best foreign film.
-
C.
bestForeignFilmHonoraryAwardRecipient
Indicates that an entity received an honorary award recognizing it as the best foreign film.
-
D.
bestPictureWinnerCountry
Indicates the country associated with the film that won the Best Picture award in a given year or context.
-
E.
academyAwardsBestPictureCount
Indicates the number of Academy Awards won for Best Picture associated with an 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deca9f1c9c8190a8b28ba0ddd3e2e3 |
completed | April 14, 2026, 11:15 p.m. |
| PD | Predicate disambiguation | batch_69de8c090d1081909b5a9bf437499d6c |
completed | April 14, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69de90c5e3a08190868680b081308c1d |
completed | April 14, 2026, 7:08 p.m. |
Created at: April 10, 2026, 1:31 a.m.