Triple
T34645065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teen Beach Movie |
E889670
|
entity |
| Predicate | fictionalFilmWithinFilm |
P51564
|
FINISHED |
| Object | Wet Side Story |
—
|
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: Wet Side Story | Statement: [Teen Beach Movie, fictionalFilmWithinFilm, Wet Side Story]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalFilmWithinFilm Context triple: [Teen Beach Movie, fictionalFilmWithinFilm, Wet Side Story]
-
A.
filmWithinFilm
chosen
Indicates that one film is depicted, referenced, or shown as existing within the narrative of another film.
-
B.
filmWithinFilmTitle
Indicates that a title refers to a fictional film that appears within another (primary) film.
-
C.
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.
-
D.
hasFictionalFrame
Indicates that one entity is presented or interpreted within the context of a fictional narrative, scenario, or imaginative framework provided by another entity.
-
E.
basedInFilm
Indicates that something (such as a character, event, or work) is situated, set, or primarily located within the context or universe 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_69f349d825c88190bfc6170ac9281260 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fec5c503808190bde0b1829ea43071 |
completed | May 9, 2026, 5:27 a.m. |
| PD | Predicate disambiguation | batch_69fec535dd6c8190a6ab85201f5a04a9 |
completed | May 9, 2026, 5:25 a.m. |
Created at: May 1, 2026, 2:04 a.m.