Triple
T34392323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Reason to Love |
E882735
|
entity |
| Predicate | influencesCharacterBehavior |
P36788
|
FINISHED |
| Object | Betty Sizemore |
—
|
NE NERFINISHED |
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: Betty Sizemore | Statement: [A Reason to Love, influencesCharacterBehavior, Betty Sizemore]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencesCharacterBehavior Context triple: [A Reason to Love, influencesCharacterBehavior, Betty Sizemore]
-
A.
influencesCharacter
chosen
Indicates that one entity affects, shapes, or alters the traits, behavior, or development of another entity’s character.
-
B.
influencesPlotOf
Indicates that one entity has an effect on or helps shape the storyline or narrative development of another entity.
-
C.
influencesThrough
Indicates that one entity affects or alters another entity indirectly by means of an intermediate factor, channel, or mechanism.
-
D.
influencesMovement
Indicates that one entity affects or alters the movement, motion, or trajectory of another entity.
-
E.
moralInfluenceOnCharacter
Indicates that one entity exerts a moral influence that shapes, guides, or alters the character or ethical disposition 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_69f349c1304081909331872829e38106 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd0d0ba5c48190bddb3f0e6637544c |
completed | May 7, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69fd0c4324a8819086c90adf46216e0e |
completed | May 7, 2026, 10:03 p.m. |
Created at: May 1, 2026, 1:59 a.m.