Triple
T32310176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Getaway |
E825476
|
entity |
| Predicate | hasSpouseRelationshipBetweenCharacters |
P138299
|
FINISHED |
| Object | Doc McCoy and Carol McCoy |
—
|
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: Doc McCoy and Carol McCoy | Statement: [The Getaway, hasSpouseRelationshipBetweenCharacters, Doc McCoy and Carol McCoy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpouseRelationshipBetweenCharacters Context triple: [The Getaway, hasSpouseRelationshipBetweenCharacters, Doc McCoy and Carol McCoy]
-
A.
spouseCharacterOf
chosen
Indicates a marital relationship where one character is the spouse of another character.
-
B.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
C.
spouseOfProtagonistOf
Indicates that one entity is the spouse (married partner) of the main character (protagonist) of another entity, typically a narrative work.
-
D.
hasSpouseActorsInLeads
Indicates that the primary leading roles in a work are performed by actors who are spouses of each other.
-
E.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
- 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_69f3491213b88190a57094d8697a7455 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fd5bf69acc819092a01e4259785dc3 |
completed | May 8, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69fd59b3f4ac8190a7f9dd3142da6e09 |
completed | May 8, 2026, 3:34 a.m. |
Created at: May 1, 2026, 12:45 a.m.