Triple
T31709623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dee Baxter |
E809281
|
entity |
| Predicate | hasRelationshipToMainCharacters |
P37304
|
FINISHED |
| Object | foil to Shawn and Marlon Wayans |
—
|
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: foil to Shawn and Marlon Wayans | Statement: [Dee Baxter, hasRelationshipToMainCharacters, foil to Shawn and Marlon Wayans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipToMainCharacters Context triple: [Dee Baxter, hasRelationshipToMainCharacters, foil to Shawn and Marlon Wayans]
-
A.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
B.
appearsAsChildOfMainCharacters
Indicates that an entity is depicted or presented as the child of the story’s main characters.
-
C.
relatedCharacter
chosen
Indicates that one character has a specified relationship or association with another character.
-
D.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
E.
hasFictionalRelationshipWith
Indicates that there exists a fictional or imagined relationship between two entities, such as characters in a story or hypothetical scenarios.
- 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_69f348df4e048190a4a5a9932ada78d6 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a019037edbc8190bdaec436ac2fcec6 |
completed | May 11, 2026, 8:15 a.m. |
| PD | Predicate disambiguation | batch_6a018fe05a34819080068f588b5ff80c |
completed | May 11, 2026, 8:14 a.m. |
Created at: April 30, 2026, 11:15 p.m.