Triple
T23969579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bunny |
E604192
|
entity |
| Predicate | belongsToCinema |
P154072
|
FINISHED |
| Object | Telugu cinema |
—
|
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: Telugu cinema | Statement: [Bunny, belongsToCinema, Telugu cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToCinema Context triple: [Bunny, belongsToCinema, Telugu cinema]
-
A.
cinemaOf
Indicates a relationship where a cinema is associated with, belongs to, or is located within a particular place, organization, or context.
-
B.
hasCinemaOperator
Indicates that a cinema is operated, managed, or run by a specific organization or individual.
-
C.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
D.
isPartOfTheater
Indicates that one entity functions as a component, section, or subdivision within a larger theater (such as a theater building, complex, or organizational unit).
-
E.
hasMovieTheater
Indicates that one entity possesses, contains, or includes a movie theater as part of its facilities or attributes.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:25 p.m.