Triple
T35332714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madame de Merret |
E1020367
|
entity |
| Predicate | emotionalOutcome |
P85012
|
FINISHED |
| Object | lifelong remorse |
—
|
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: lifelong remorse | Statement: [Madame de Merret, emotionalOutcome, lifelong remorse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalOutcome Context triple: [Madame de Merret, emotionalOutcome, lifelong remorse]
-
A.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
-
B.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
C.
emotionalTrajectory
chosen
Indicates how an entity’s emotional state changes or progresses over time in relation to another entity or context.
-
D.
emotionalTrait
Indicates that an entity possesses a particular emotional characteristic, disposition, or affective quality.
-
E.
emotionalCounterpartOf
Indicates that one entity serves as the emotional equivalent, complement, or matching emotional role 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_69f76deacf4481908e7735a5a7715b0a |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.