Triple
T32280651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Before trilogy |
E824678
|
entity |
| Predicate | recurringTimeGapBetweenFilms |
P157871
|
FINISHED |
| Object | approximately 9 years |
—
|
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: approximately 9 years | Statement: [Before trilogy, recurringTimeGapBetweenFilms, approximately 9 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recurringTimeGapBetweenFilms Context triple: [Before trilogy, recurringTimeGapBetweenFilms, approximately 9 years]
-
A.
settingTimeRelativeToFilms
Indicates the temporal relationship of one film’s setting relative to the settings of other films (e.g., earlier, later, or at the same time).
-
B.
storyTimeSpanInFilm
Indicates the duration of time that the story or narrative covers within the film.
-
C.
narrativeTimeGapFromPrequel
chosen
Indicates that there is a temporal gap in the story’s timeline between this work and its prequel.
-
D.
marksReturnToCinemaAfterYears
Indicates that an event or work signifies a person's or entity's comeback to cinema after being absent for several years.
-
E.
timeToFilmAdaptation
Indicates the amount of time that passes between the original work’s release and the release of its film adaptation.
- 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_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bcc7edfc81909d0008fa9364de54 |
completed | May 3, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69f6b632cf788190a3d0c08cd026b84b |
completed | May 3, 2026, 2:42 a.m. |
Created at: May 1, 2026, 12:43 a.m.