Triple
T9211070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cecilia Parker |
E221116
|
entity |
| Predicate | fictionalRelationshipPortrayed |
P34570
|
FINISHED |
| Object | sister of Andy Hardy |
—
|
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: sister of Andy Hardy | Statement: [Cecilia Parker, fictionalRelationshipPortrayed, sister of Andy Hardy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalRelationshipPortrayed Context triple: [Cecilia Parker, fictionalRelationshipPortrayed, sister of Andy Hardy]
-
A.
fictionalRelationship
chosen
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
B.
portraysRelationship
Indicates that one entity depicts, represents, or illustrates a relationship between other entities.
-
C.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
D.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
E.
showsRelationshipWith
Indicates that one entity visually or explicitly presents or demonstrates its connection or association with 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9b54520819087030148dadd6385 |
completed | April 1, 2026, 8:39 a.m. |
| PD | Predicate disambiguation | batch_69cc660af2408190ae06eb8326e1c64e |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:27 p.m.