Triple
T25959372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munna |
E645493
|
entity |
| Predicate | mainAntagonistOccupation |
P22239
|
FINISHED |
| Object | mafia leader |
—
|
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: mafia leader | Statement: [Munna, mainAntagonistOccupation, mafia leader]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainAntagonistOccupation Context triple: [Munna, mainAntagonistOccupation, mafia leader]
-
A.
antagonistOccupation
chosen
Indicates the role, job, or professional activity that the antagonist character performs.
-
B.
mainAntagonistPortrayedBy
Indicates that the person is the primary actor who plays the main antagonist character in a work.
-
C.
missionOfAntagonist
Indicates the primary goal, plan, or objective that the antagonist is actively pursuing.
-
D.
primaryAntagonistType
Indicates the role or category of the main opposing force or adversary that serves as the central source of conflict.
-
E.
antagonistActorRole
Indicates that an actor plays the role of an antagonist in a given work or context.
- 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_69e77e85efc08190997da7fcf98bd300 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f65aa07c048190a5df30d53d8f0cf5 |
completed | May 2, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 22, 2026, 8:47 a.m.