Triple
T33209891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marlee |
E850122
|
entity |
| Predicate | filmCharacterRelationshipWith |
P38921
|
FINISHED |
| Object | Nicholas Easter (played by John Cusack) |
—
|
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: Nicholas Easter (played by John Cusack) | Statement: [Marlee, filmCharacterRelationshipWith, Nicholas Easter (played by John Cusack)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmCharacterRelationshipWith Context triple: [Marlee, filmCharacterRelationshipWith, Nicholas Easter (played by John Cusack)]
-
A.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
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.
portraysCharacterRelationship
Indicates that one entity depicts or represents the relationship between characters in another entity.
-
E.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between 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_69f3495fb92c819083ce65d0ddee7a76 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dd3cc0648190a275812d6711275a |
completed | May 3, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69f6d82eaee081908f06a71546315aea |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:30 a.m.