Triple
T11064141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Film Award for Best Feature Film in English |
E261578
|
entity |
| Predicate | languageOfEligibleFilms |
P93753
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [National Film Award for Best Feature Film in English, languageOfEligibleFilms, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfEligibleFilms Context triple: [National Film Award for Best Feature Film in English, languageOfEligibleFilms, English]
-
A.
notableLanguageOfEligibleFilms
Indicates that there is a notable language associated with the set of films that qualify as eligible under a given criterion or program.
-
B.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
C.
notableLanguageOfEligiblePrograms
Indicates that the specified language is a significant or primary language used in the programs that qualify under certain eligibility criteria.
-
D.
primaryFilmingLanguage
chosen
Indicates the main language in which a film or audiovisual work was originally filmed or recorded.
-
E.
languageDubbedIn
Indicates that the content’s audio has been dubbed into the specified language.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798ed07f88190bf501d9f63386ada |
completed | April 9, 2026, 12:17 p.m. |
| PD | Predicate disambiguation | batch_69d74411d9e881908c0eeafa0f38e4b6 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.