Triple
T1652464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Tobias |
E35722
|
entity |
| Predicate | hasAdversaryInStory |
P17627
|
FINISHED |
| Object | rapists |
—
|
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: rapists | Statement: [Sarah Tobias, hasAdversaryInStory, rapists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdversaryInStory Context triple: [Sarah Tobias, hasAdversaryInStory, rapists]
-
A.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
B.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
C.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
D.
hasAntagonistGroup
chosen
Indicates that an entity is opposed or challenged by a specific group acting as its antagonist.
-
E.
notableAdversary
Indicates that one entity is recognized as a significant or prominent opponent or rival of 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaa0fbe984819084f8daee81ca9b67 |
completed | March 6, 2026, 9:40 a.m. |
| PD | Predicate disambiguation | batch_69a907ce4dd881909168a1e99505d4ec |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:29 p.m.