Triple
T7659634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bubba Bexley |
E173470
|
entity |
| Predicate | hasRelationshipToMainCharacter |
P38921
|
FINISHED |
| Object | friend of the protagonist Fred Sanford |
—
|
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: friend of the protagonist Fred Sanford | Statement: [Bubba Bexley, hasRelationshipToMainCharacter, friend of the protagonist Fred Sanford]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipToMainCharacter Context triple: [Bubba Bexley, hasRelationshipToMainCharacter, friend of the protagonist Fred Sanford]
-
A.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
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.
relativeOfMainCharacter
Indicates that one entity is a family member or relative of the main character 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 3:59 p.m.