Triple
T12915415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Darrow |
E308967
|
entity |
| Predicate | relationshipToTitleCharacter |
P38921
|
FINISHED |
| Object | object of King Kong's affection |
—
|
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: object of King Kong's affection | Statement: [Ann Darrow, relationshipToTitleCharacter, object of King Kong's affection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToTitleCharacter Context triple: [Ann Darrow, relationshipToTitleCharacter, object of King Kong's affection]
-
A.
titleCharacterRelation
Indicates the relationship between a work’s title and a specific character it references or centers on.
-
B.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
C.
relatedCharacter
Indicates that one character has a specified relationship or association with another character.
-
D.
relatedCharacterType
Indicates that one character has a specified type of relationship or role in connection to another character.
-
E.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a0d6508190bca9668e9e06abfe |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.