Triple
T25233401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gang Leader |
E632273
|
entity |
| Predicate | basedInFilmIndustry |
P21046
|
FINISHED |
| Object | Telugu cinema |
—
|
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: Telugu cinema | Statement: [Gang Leader, basedInFilmIndustry, Telugu cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedInFilmIndustry Context triple: [Gang Leader, basedInFilmIndustry, Telugu cinema]
-
A.
originatesInFilmIndustry
Indicates that something has its source, development, or primary origin within the film industry.
-
B.
basedInFilm
Indicates that something (such as a character, event, or work) is situated, set, or primarily located within the context or universe of a particular film.
-
C.
hasFilmIndustryCenter
chosen
Indicates that a location serves as a primary hub or central base for activities related to the film industry.
-
D.
occupationInFilm
Indicates that an entity has a specific occupation or role within the context of a particular film.
-
E.
hasRelativeInEntertainmentIndustry
Indicates that one entity has a family member who works in the entertainment industry.
- 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_69e75a8ec5f88190b9eba06ae42b413a |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f61f12b0f08190bc4a16907941864c |
completed | May 2, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69f61b37a5648190b10d33ae205ccfee |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 21, 2026, 1:06 p.m.