Triple
T21315286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Watch Over Me |
E525452
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Jeffrey Lerner |
—
|
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 Lerner | Statement: [Watch Over Me, executiveProducer, Jeffrey Lerner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Lerner Context triple: [Watch Over Me, executiveProducer, Jeffrey Lerner]
-
A.
Jeffrey Lerner
chosen
Jeffrey Lerner is a film and television producer known for his work as an executive producer on various projects, including the movie "Watch Over Me."
-
B.
Jeffrey Levine
Jeffrey Levine is a film producer known for his work on the comedy-drama movie "This Is Where I Leave You."
-
C.
Michael D. Rosenthal
Michael D. Rosenthal is a writer best known as the author whose work inspired the "Twilight Zone" episode "A Kind of Stopwatch."
-
D.
Neil B. Shulman
Neil B. Shulman is an American physician and author best known for writing the novel that inspired the film "Doc Hollywood."
-
E.
Stephen Wainger
Stephen Wainger is an American mathematician known for his contributions to harmonic analysis and related areas of analysis.
- 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_69e0b51ad810819098c12392c8e55f6c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75dcf2534819097abbb2e9559e791 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 16, 2026, 4:28 p.m.