Triple
T19183071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | In the Mix |
E469625
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Cheryl Edwards |
—
|
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: Cheryl Edwards | Statement: [In the Mix, screenwriter, Cheryl Edwards]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cheryl Edwards Context triple: [In the Mix, screenwriter, Cheryl Edwards]
-
A.
Cheryl Edwards
chosen
Cheryl Edwards is a screenwriter best known for her work on the film "In the Mix."
-
B.
Cheryl James
Cheryl James, better known as “Salt,” is an American rapper and one-third of the pioneering, Grammy-winning hip hop group Salt-N-Pepa.
-
C.
Cheryl Bentyne
Cheryl Bentyne is an American jazz singer best known as a longtime member of the vocal group The Manhattan Transfer.
-
D.
Cheryl Holdridge
Cheryl Holdridge was an American actress best known as one of the original Mouseketeers on Disney's "The Mickey Mouse Club."
-
E.
Cheryl Thomas
Cheryl Thomas is a prominent legal scholar and professor at University College London known for her influential research on juries, judicial decision-making, and the justice system.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f61e1c248190ba9e220c1be61ef8 |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 10, 2026, 12:07 p.m.