Triple
T28921576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rebus |
E733527
|
entity |
| Predicate | leadActorInEarlyAdaptations |
P166850
|
FINISHED |
| Object | John Hannah |
—
|
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: John Hannah | Statement: [Rebus, leadActorInEarlyAdaptations, John Hannah]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorInEarlyAdaptations Context triple: [Rebus, leadActorInEarlyAdaptations, John Hannah]
-
A.
leadActorOfAdaptation
Indicates that a person is the main actor in a specific adaptation of a work (such as a film, series, or stage version).
-
B.
leadRoleActor
chosen
Indicates that an actor performs a leading or principal role in a work or production.
-
C.
originalLeadActorRole
Indicates the role originally played by a particular lead actor in a given production or work.
-
D.
playedEarlyRoleIn
Indicates that one entity contributed significantly to the initial or formative stages of another entity’s development, success, or emergence.
-
E.
filmAdaptationEvaPerformer
Indicates that the subject is a performer who played the character Eva in a film adaptation of the referenced work.
- 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_69f05b0a5cc0819094828367ae204b70 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f66d7765208190b87b1cc6d96a151c |
completed | May 2, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 28, 2026, 8:20 a.m.