Triple
T12081315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Sloper |
E287683
|
entity |
| Predicate | emotionalResponseToBetrayal |
P85012
|
FINISHED |
| Object | withdrawal |
—
|
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: withdrawal | Statement: [Catherine Sloper, emotionalResponseToBetrayal, withdrawal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalResponseToBetrayal Context triple: [Catherine Sloper, emotionalResponseToBetrayal, withdrawal]
-
A.
betrayed
Indicates that one entity has broken the trust, loyalty, or confidence of another, typically by acting against their interests or revealing something meant to be kept secret.
-
B.
emotionalTrigger
Indicates that one entity causes or elicits an emotional response or reaction in another entity.
-
C.
emotionallyAbuses
Indicates that one entity subjects another to harmful, manipulative, or degrading emotional treatment.
-
D.
reactionToAffair
Indicates how an entity responds emotionally or behaviorally to an affair or infidelity involving another entity.
-
E.
emotionalTrajectory
chosen
Indicates how an entity’s emotional state changes or progresses over time in relation to another entity or context.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bf4f508190842927e7e0642235 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:48 p.m.