Triple
T19452841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzanne Warren |
E486655
|
entity |
| Predicate | hasConflictWith |
P4897
|
FINISHED |
| Object | Vee Parker |
—
|
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: Vee Parker | Statement: [Suzanne Warren, hasConflictWith, Vee Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vee Parker Context triple: [Suzanne Warren, hasConflictWith, Vee Parker]
-
A.
Vee Parker
chosen
Vee Parker is a manipulative and influential older inmate in "Orange Is the New Black" who exerts control over younger prisoners, including Taystee.
-
B.
Weckeah Parker
Weckeah Parker was one of the wives of Quanah Parker, the last principal chief of the Comanche, and a member of his prominent Comanche family.
-
C.
Parker Stevenson
Parker Stevenson is an American actor best known for his roles in the television series "The Hardy Boys/Nancy Drew Mysteries" and "Baywatch."
-
D.
Tej Parker
Tej Parker is a tech-savvy mechanic and hacker in the Fast & Furious film franchise, known for his intelligence, humor, and close partnership with Roman Pearce.
-
E.
Parker Sawyers
Parker Sawyers is an American actor best known for portraying a young Barack Obama in the film "Southside with You" and for his work in various international film and television productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6339407a08190a3e0213bfbb4df3d |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 10, 2026, 1:38 p.m.