Triple
T23679226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telugu people |
E584971
|
entity |
| Predicate | cinemaIndustry |
P6393
|
FINISHED |
| Object | Tollywood |
—
|
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: Tollywood | Statement: [Telugu people, cinemaIndustry, Tollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cinemaIndustry Context triple: [Telugu people, cinemaIndustry, Tollywood]
-
A.
popularFilmIndustry
chosen
Indicates that an entity has a widely recognized and well-liked film industry that attracts significant audience interest and attention.
-
B.
cinemaCategory
Indicates the classification or genre category assigned to a cinema or film.
-
C.
cinemaInfluence
Indicates how one entity affects or shapes another through the medium of cinema, such as films, filmmaking, or cinematic culture.
-
D.
cinemaOf
Indicates a relationship where a cinema is associated with, belongs to, or is located within a particular place, organization, or context.
-
E.
filmMedium
Indicates the physical or technical format (such as film stock, digital, or video) in which a film is recorded or presented.
- 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_69e24901f7c08190909fd727632e823d |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b4f5a7b48190b93d27416003e173 |
completed | April 29, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f118dd13008190a8799b4e9cadbd79 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:51 p.m.