Triple
T33060256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Big Bounce |
E845951
|
entity |
| Predicate | hasFilmReception |
P181183
|
FINISHED |
| Object | generally negative for adaptations |
—
|
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: generally negative for adaptations | Statement: [The Big Bounce, hasFilmReception, generally negative for adaptations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmReception Context triple: [The Big Bounce, hasFilmReception, generally negative for adaptations]
-
A.
hasFilmScore
Indicates that one entity serves as the musical score or soundtrack composed for a particular film.
-
B.
hasTheatricalCriticism
Indicates that one entity has written, produced, or is otherwise associated with theatrical criticism about another entity.
-
C.
hasReceivedCriticalAcclaim
Indicates that the subject has been widely praised or positively recognized by critics or expert reviewers.
-
D.
hasFilmScoreBy
Indicates that a film’s musical score was composed or created by a specified person or entity.
-
E.
mediaReception
chosen
Indicates how a piece of media is received, evaluated, or responded to by audiences, critics, or other observers.
- 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_69f3495333b8819095e9af56855b9061 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff0491409c8190be40f633a58da0b1 |
completed | May 9, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69ff040bb5cc81909534c7eee85d5e90 |
completed | May 9, 2026, 9:53 a.m. |
Created at: May 1, 2026, 1:25 a.m.