Triple
T29558278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Citizen |
E749964
|
entity |
| Predicate | basedInFilmIndustryOf |
P21046
|
FINISHED |
| Object | Tamil 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: Tamil cinema | Statement: [Citizen, basedInFilmIndustryOf, Tamil cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedInFilmIndustryOf Context triple: [Citizen, basedInFilmIndustryOf, Tamil cinema]
-
A.
originatesInFilmIndustry
Indicates that something has its source, development, or primary origin within the film industry.
-
B.
sangForFilmIndustry
Indicates that a person performed singing specifically for use in the film industry, such as in movies or film soundtracks.
-
C.
hasFilmIndustryCenter
chosen
Indicates that a location serves as a primary hub or central base for activities related to the film industry.
-
D.
workedOnFilmReleasedBy
Indicates that one entity contributed work to a film that was distributed or released by another entity.
-
E.
occupationInFilm
Indicates that an entity has a specific occupation or role within the context of a particular film.
- 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_69f0bd4919e48190942b2a13d5b97d03 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fd44474ed48190ac372e4c88d762ed |
completed | May 8, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69fd41ef28a48190a66959be5c964461 |
completed | May 8, 2026, 1:52 a.m. |
Created at: April 28, 2026, 5:18 p.m.