Triple
T25490898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temper |
E638832
|
entity |
| Predicate | featuresCorruptCopProtagonist |
P31758
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Temper, featuresCorruptCopProtagonist, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCorruptCopProtagonist Context triple: [Temper, featuresCorruptCopProtagonist, true]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
hasCorruptBusinessmanCharacter
Indicates that a work includes a character who is a businessman engaged in corrupt or unethical activities.
-
C.
policeCharacter
chosen
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
D.
featuresAntagonistEntity
Indicates that the subject includes or involves an entity serving as an antagonist in the context of a narrative, interaction, or scenario.
-
E.
coProtagonist
Indicates that two or more entities share the primary leading role together in the same narrative 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_69e75dbbd2a88190b70e1e645de14b9a |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6e6029a10819098ff21f58079e70e |
completed | May 3, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d5e8188190b1e1c2e5d1b77031 |
completed | May 3, 2026, 5:57 a.m. |
Created at: April 21, 2026, 2:38 p.m.