Triple
T37125261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | That Kind of Woman |
E919371
|
entity |
| Predicate | leadActorForCharacterRed |
P191731
|
FINISHED |
| Object | Tab Hunter |
—
|
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: Tab Hunter | Statement: [That Kind of Woman, leadActorForCharacterRed, Tab Hunter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorForCharacterRed Context triple: [That Kind of Woman, leadActorForCharacterRed, Tab Hunter]
-
A.
leadRoleActor
Indicates that an actor performs a leading or principal role in a work or production.
-
B.
leadActorForCharacterKay
Indicates that one entity is the lead actor portraying the character Kay in a given production.
-
C.
leadActorRolePattern
Indicates a recurring or characteristic type of role that an actor typically plays as a leading performer in productions.
-
D.
leadCharacterBasedOn
Indicates that a lead character is derived from, inspired by, or adapted from a particular source entity (such as a real person, another character, or existing work).
-
E.
leadActorOfAdaptation
Indicates that a person is the main actor in a specific adaptation of a work (such as a film, series, or stage version).
- 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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fce7671f108190bf3ebf54339068b5 |
completed | May 7, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69fce5b5a84c81908ac1b5b9f08d48d0 |
completed | May 7, 2026, 7:19 p.m. |
| PDg | Predicate description generation | batch_69fce76669408190b1beef8899a8a5e2 |
completed | May 7, 2026, 7:26 p.m. |
Created at: May 3, 2026, 4:15 p.m.