Triple
T22161336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dankook University |
E547675
|
entity |
| Predicate | hasTheaterAndFilmProgram |
P147207
|
FINISHED |
| Object | undergraduate major in Theater |
—
|
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: undergraduate major in Theater | Statement: [Dankook University, hasTheaterAndFilmProgram, undergraduate major in Theater]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTheaterAndFilmProgram Context triple: [Dankook University, hasTheaterAndFilmProgram, undergraduate major in Theater]
-
A.
hasTheatricalFilm
Indicates that an entity has an associated theatrical film adaptation, version, or release.
-
B.
hasFilmScreenings
Indicates that a film is scheduled to be shown at one or more specific screenings or venues.
-
C.
hasCulturalProgram
Indicates that an entity offers or participates in an organized set of cultural activities, events, or initiatives.
-
D.
hasFilmFestival
Indicates that a place, organization, or context hosts, organizes, or is the venue for a film festival.
-
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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a2d8064819094d27ef9f15c6a1f |
completed | April 28, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69e71b41555881909b8e22718974d527 |
completed | April 21, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e7222d208c819098b12c13e31af629 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 8:34 p.m.