Triple
T23444378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Caine as John Tarrant |
E565493
|
entity |
| Predicate | facesAntagonists |
P93370
|
FINISHED |
| Object | terrorists |
—
|
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: terrorists | Statement: [Michael Caine as John Tarrant, facesAntagonists, terrorists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facesAntagonists Context triple: [Michael Caine as John Tarrant, facesAntagonists, terrorists]
-
A.
facesAntagonistType
chosen
Indicates that an entity confronts or opposes an antagonist of a specified type.
-
B.
featuresAntagonistEntity
Indicates that the subject includes or involves an entity serving as an antagonist in the context of a narrative, interaction, or scenario.
-
C.
leadAntagonistCharacter
Indicates that one character serves as the primary opposing or villainous force in relation to another entity in the narrative.
-
D.
mainAntagonistPortrayedBy
Indicates that the person is the primary actor who plays the main antagonist character in a work.
-
E.
primaryAntagonists
Indicates that the referenced entities serve as the main opposing or adversarial forces in relation to a specified subject or narrative.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64717d08190a2c25e7bbfc17a2f |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:51 p.m.