Triple
T35179312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 35 Up |
E1015802
|
entity |
| Predicate | recurringIntervalWithOtherFilms |
P121478
|
FINISHED |
| Object | 7 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: 7 years | Statement: [35 Up, recurringIntervalWithOtherFilms, 7 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recurringIntervalWithOtherFilms Context triple: [35 Up, recurringIntervalWithOtherFilms, 7 years]
-
A.
recurringDuring
Indicates that an event or state happens repeatedly within the time span or context defined by another event or interval.
-
B.
hasRecurringSeriesProtagonists
Indicates that a recurring series features one or more protagonists who appear repeatedly across its installments.
-
C.
hasSequelShotBackToBackWith
Indicates that two sequels were filmed consecutively or simultaneously as part of the same production schedule.
-
D.
recurringSeries
chosen
Indicates that an event, action, or pattern occurs repeatedly over time as part of an ongoing series rather than as a one-time instance.
-
E.
hasRecurringActor
Indicates that an actor appears repeatedly across multiple instances or episodes within a work or series.
- 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_69f76ddcc108819097f96853b7ed9ef4 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78f63c8788190b253a18de5ca1312 |
completed | May 3, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69f78e2d71248190b850c2802ec170c0 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:02 p.m.