Triple
T33510888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roller Boogie |
E858238
|
entity |
| Predicate | leadActorBackground |
P52443
|
FINISHED |
| Object | Jim Bray was a professional roller skater |
—
|
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: Jim Bray was a professional roller skater | Statement: [Roller Boogie, leadActorBackground, Jim Bray was a professional roller skater]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorBackground Context triple: [Roller Boogie, leadActorBackground, Jim Bray was a professional roller skater]
-
A.
actorBackground
chosen
Indicates that one entity provides contextual or biographical background information about an actor entity.
-
B.
leadRoleActor
Indicates that an actor performs a leading or principal role in a work or production.
-
C.
leadActorParent
Indicates that one entity is the parent of the lead actor associated with another entity (such as a film, show, or performance).
-
D.
leadActorOfAdaptation
Indicates that a person is the main actor in a specific adaptation of a work (such as a film, series, or stage version).
-
E.
leadActorLaterOffice
Indicates that an individual who was the lead actor in a work later held a formal office or official position.
- 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_69f3497721848190978fbee5e0a526f8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7886be6d8819095ec62e4f2cee858 |
completed | May 3, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69f7841440f48190b4346c08855951d2 |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 1, 2026, 1:38 a.m.