Triple
T22269011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Original Soundtracks 1 |
E550425
|
entity |
| Predicate | hasImaginaryFilmCredits |
P147613
|
FINISHED |
| Object | Yes |
—
|
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: Yes | Statement: [Original Soundtracks 1, hasImaginaryFilmCredits, Yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImaginaryFilmCredits Context triple: [Original Soundtracks 1, hasImaginaryFilmCredits, Yes]
-
A.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
B.
hasWorkedOnFilmBy
Indicates that one entity has worked on a film that was created, directed, or otherwise authored by another entity.
-
C.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
D.
hasFictionalFilmWithinPlay
Indicates that within a theatrical play, there is a fictional film that exists or is depicted as part of the play’s narrative or structure.
-
E.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
- 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_69e11e43d8208190aff4f9cf7f2c2a8a |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f141bde9f88190b533bcb88787b492 |
completed | April 28, 2026, 11:24 p.m. |
| PD | Predicate disambiguation | batch_69e72ff0363081909f794d19c8a64837 |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342ce08c8190bc0a7085f4a952e7 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:40 p.m.