Triple
T16456997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pay It Forward |
E399708
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Robert L. Levy |
—
|
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: Robert L. Levy | Statement: [Pay It Forward, producer, Robert L. Levy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert L. Levy Context triple: [Pay It Forward, producer, Robert L. Levy]
-
A.
Robert L. Levy
chosen
Robert L. Levy is a film producer best known for his work on popular Hollywood comedies, including the hit movie "Wedding Crashers."
-
B.
Benn W. Levy
Benn W. Levy was a British playwright, screenwriter, and politician known for his work in early sound cinema and his later career as a Labour Member of Parliament.
-
C.
Barry L. Levy
Barry L. Levy is an American screenwriter best known for writing the political thriller film "Vantage Point."
-
D.
Fredric G. Levin
Fredric G. Levin was a prominent American trial lawyer and philanthropist known for his success in high-stakes personal injury and tobacco litigation.
-
E.
David F. Levi
David F. Levi is an American legal scholar and former federal judge who served as dean of Duke University School of Law.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d7dfd188190b03e9b4151a4d3d8 |
completed | April 18, 2026, 7:06 a.m. |
Created at: April 10, 2026, 5:10 a.m.