Triple
T35203794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tusk of Ganesh |
E1016478
|
entity |
| Predicate | antagonistMotivation |
P91485
|
FINISHED |
| Object | political leverage for Asav |
—
|
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: political leverage for Asav | Statement: [Tusk of Ganesh, antagonistMotivation, political leverage for Asav]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonistMotivation Context triple: [Tusk of Ganesh, antagonistMotivation, political leverage for Asav]
-
A.
missionOfAntagonist
Indicates the primary goal, plan, or objective that the antagonist is actively pursuing.
-
B.
antagonistActionOf
Indicates that one entity performs an action in opposition or hostility toward another entity, acting as its antagonist.
-
C.
characterMotivation
chosen
Indicates the underlying reasons, desires, or goals that drive a character’s actions and decisions within a narrative.
-
D.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
E.
antagonistMethod
Indicates the method or strategy an antagonist uses to oppose, harm, or create conflict with another entity.
- 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_69f76dde814c8190a71f60d514a424a4 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78f63c8788190b253a18de5ca1312 |
completed | May 3, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69f78e2d71248190b850c2802ec170c0 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:02 p.m.