Triple
T21382454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Hills |
E527394
|
entity |
| Predicate | takesRevengeOn |
P123613
|
FINISHED |
| Object | her attackers |
—
|
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: her attackers | Statement: [Jennifer Hills, takesRevengeOn, her attackers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: takesRevengeOn Context triple: [Jennifer Hills, takesRevengeOn, her attackers]
-
A.
methodOfRevenge
Indicates a relationship where a specific action or strategy is used as the means by which revenge is carried out.
-
B.
causeOfVengeance
Indicates a relationship where one entity is the reason or trigger for another entity’s desire or act of vengeance.
-
C.
positionOnRevenge
Indicates a stance or attitude that one entity holds regarding the idea, acceptability, or pursuit of revenge.
-
D.
antagonistActionOf
chosen
Indicates that one entity performs an action in opposition or hostility toward another entity, acting as its antagonist.
-
E.
antagonistOf
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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0d1fa1c8190b3374e0bb3a971fc |
completed | April 22, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:12 p.m.