Triple
T25654933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Bengal Rahasya |
E643206
|
entity |
| Predicate | hasAntagonisticElement |
P18963
|
FINISHED |
| Object | tiger |
—
|
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: tiger | Statement: [Royal Bengal Rahasya, hasAntagonisticElement, tiger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAntagonisticElement Context triple: [Royal Bengal Rahasya, hasAntagonisticElement, tiger]
-
A.
hasOppositionalElements
Indicates that something contains components or aspects that are in conflict, contrast, or opposition to each other.
-
B.
hasAntagonistForm
Indicates that an entity possesses or takes on a form characterized by opposition, hostility, or antagonistic behavior toward another entity.
-
C.
hasAntagonistGroup
Indicates that an entity is opposed or challenged by a specific group acting as its antagonist.
-
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.
antagonistOf
chosen
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
- 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_69e77e7d8a848190a98d0162325fd780 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f650c70d7c819093d9a0f005f7c8d5 |
completed | May 2, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69f64cab1f648190a2a9460690d18a37 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 21, 2026, 6:31 p.m.