Triple
T18731314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Farewell (2019 film) |
E458040
|
entity |
| Predicate | producedBy |
P490
|
FINISHED |
| Object | Peter Saraf |
—
|
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: Peter Saraf | Statement: [The Farewell (2019 film), producedBy, Peter Saraf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Saraf Context triple: [The Farewell (2019 film), producedBy, Peter Saraf]
-
A.
Peter Saraf
chosen
Peter Saraf is an American film producer known for his work on acclaimed independent and mainstream films, including "A Beautiful Day in the Neighborhood."
-
B.
Roshan Sethi
Roshan Sethi is a physician-turned-screenwriter and television producer best known for co-creating the medical drama series "The Resident."
-
C.
Manish Dayal
Manish Dayal is an American actor best known for his leading role in the film "The Hundred-Foot Journey" and for his work in television series such as "The Resident."
-
D.
Michael Bhaskar
Michael Bhaskar is a British writer, publisher, and technology theorist known for his work on the impact of digital innovation and artificial intelligence on society and the future.
-
E.
Akash Khurana
Akash Khurana is an Indian actor, screenwriter, and director known for his work in Hindi cinema 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d7854748190b66c4aaadfd67f29 |
completed | April 20, 2026, 12:04 a.m. |
Created at: April 10, 2026, 11:51 a.m.