Triple
T33403315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine (ex‑wife) |
E855371
|
entity |
| Predicate | emotionalImpactOnProtagonist |
P177018
|
FINISHED |
| Object | significant |
—
|
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: significant | Statement: [Catherine (ex‑wife), emotionalImpactOnProtagonist, significant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalImpactOnProtagonist Context triple: [Catherine (ex‑wife), emotionalImpactOnProtagonist, significant]
-
A.
protagonistReaction
Indicates how a main character responds emotionally or behaviorally to a particular event, situation, or stimulus.
-
B.
emotionalTrajectory
Indicates how an entity’s emotional state changes or progresses over time in relation to another entity or context.
-
C.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
-
D.
emotionalChallenge
Indicates a situation where one entity causes or experiences significant emotional difficulty or stress in relation to another entity or circumstance.
-
E.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
- F. None of above. chosen
Provenance (4 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_69f3496e3f1c8190bcecfa82aa9d17ff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f85bfba48190aba95b40642a8ca7 |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f814fcf48190ae4504154d1b2c05 |
completed | May 3, 2026, 7:24 a.m. |
Created at: May 1, 2026, 1:36 a.m.