Triple
T22712892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Vaughan with Clifford Brown |
E561647
|
entity |
| Predicate | track |
P17929
|
FINISHED |
| Object | Jim |
—
|
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: Jim | Statement: [Sarah Vaughan with Clifford Brown, track, Jim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Context triple: [Sarah Vaughan with Clifford Brown, track, Jim]
-
A.
Jim
Jim is a character in the first act of the play "Clybourne Park," which explores themes of race, property, and social change in a Chicago neighborhood.
-
B.
Jim
chosen
Jim is a common English given name, typically used as a diminutive or familiar form of James.
-
C.
Joe
"Joe" is a 1970 American drama film, directed by John G. Avildsen, that explores class conflict and reactionary violence through the unlikely alliance between a wealthy executive and a bigoted factory worker.
-
D.
Joe
Joe is the given name of American actor and comedian Joe Torry, known for his roles in films and television during the 1990s.
-
E.
Joe
Joe is a fictional character played by American actor Hector Elizondo, known from his roles in film and television.
- 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_69e2454f1348819088d83f420925a5c1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1790ab6208190a342f076002324ab |
completed | April 29, 2026, 3:20 a.m. |
Created at: April 17, 2026, 3:18 p.m.