Triple
T23970325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stylish Star |
E604209
|
entity |
| Predicate | associatedWithLanguageFilmIndustry |
P101250
|
FINISHED |
| Object | Telugu-language films |
—
|
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-language films | Statement: [Stylish Star, associatedWithLanguageFilmIndustry, Telugu-language films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithLanguageFilmIndustry Context triple: [Stylish Star, associatedWithLanguageFilmIndustry, Telugu-language films]
-
A.
associatedWithProducerOfFilm
Indicates that one entity has an association or connection with the producer of a particular film.
-
B.
associatedWithComposerOfFilm
Indicates a relationship where an entity is connected to the composer who created the musical score for a specific film.
-
C.
originatesInFilmIndustry
chosen
Indicates that something has its source, development, or primary origin within the film industry.
-
D.
workLanguageOfTitle
Indicates the language in which a specific work or title is expressed or written.
-
E.
associatedWithLeadActorOfFilm
Indicates a relationship where one entity is connected or linked in some relevant way to the lead actor of a specified 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_69e29543019c8190872462e593cc50b4 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d1db392c8190a1044b75b898243a |
completed | April 29, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:25 p.m.