Triple
T25850973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kate McCoy |
E651201
|
entity |
| Predicate | familyRelationInFiction |
P106091
|
FINISHED |
| Object | member of the McCoy family |
—
|
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: member of the McCoy family | Statement: [Kate McCoy, familyRelationInFiction, member of the McCoy family]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: familyRelationInFiction Context triple: [Kate McCoy, familyRelationInFiction, member of the McCoy family]
-
A.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
B.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
C.
politicalRelationship
Indicates a relationship in which entities are connected through political roles, alliances, affiliations, or interactions within a political context.
-
D.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
E.
relatedToInFiction
chosen
Indicates that one entity is connected to another within a fictional context, such as a story, universe, or narrative 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_69e7ab39035c8190be15c8aaee1bb858 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 22, 2026, 7:58 a.m.