Triple
T22102700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Night Listener |
E546209
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jeffrey Sharp |
—
|
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: Jeffrey Sharp | Statement: [The Night Listener, producer, Jeffrey Sharp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Sharp Context triple: [The Night Listener, producer, Jeffrey Sharp]
-
A.
Jeffrey Sharp
chosen
Jeffrey Sharp is an American film producer known for his work on acclaimed independent movies and literary adaptations.
-
B.
Kevin Sharp
Kevin Sharp is a name shared by several notable individuals, including an American country music singer and a British judge.
-
C.
Jeffrey Watts
Jeffrey Watts is an American jazz drummer known for his innovative work with the Wynton Marsalis Quartet and the Branford Marsalis Quartet.
-
D.
Michael Sharp
Michael Sharp is a relatively common personal name that may refer to multiple individuals across different fields, such as academia, the arts, or public service.
-
E.
Jeffrey Heath
Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129163b908190b63ace06016f4db8 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.