Triple
T29212590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Padayappa |
E740581
|
entity |
| Predicate | leadAntagonistRole |
P113106
|
FINISHED |
| Object | Ramya Krishnan as Neelambari |
—
|
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: Ramya Krishnan as Neelambari | Statement: [Padayappa, leadAntagonistRole, Ramya Krishnan as Neelambari]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadAntagonistRole Context triple: [Padayappa, leadAntagonistRole, Ramya Krishnan as Neelambari]
-
A.
leadAntagonistCharacter
Indicates that one character serves as the primary opposing or villainous force in relation to another entity in the narrative.
-
B.
antagonistActorRole
Indicates that an actor plays the role of an antagonist in a given work or context.
-
C.
antagonistOccupation
Indicates the role, job, or professional activity that the antagonist character performs.
-
D.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
-
E.
mainAntagonistPortrayedBy
chosen
Indicates that the person is the primary actor who plays the main antagonist character in a 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_69f07cba2f808190a2746477d4e8345b |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f68a16debc8190a12f5f65ced055d7 |
completed | May 2, 2026, 11:34 p.m. |
| PD | Predicate disambiguation | batch_69f6860def1c81909d79e1f088c4b5e5 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 28, 2026, 12:12 p.m.