Triple
T23023105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loser |
E573222
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Brad Epstein |
—
|
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: Brad Epstein | Statement: [Loser, producer, Brad Epstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brad Epstein Context triple: [Loser, producer, Brad Epstein]
-
A.
Brad Epstein
chosen
Brad Epstein is a film producer best known for his work on the romantic comedy-drama "Dan in Real Life."
-
B.
Bob Epstein
Bob Epstein is a computer scientist and entrepreneur best known as a co-founder of the database software company Sybase.
-
C.
Harold Lipstein
Harold Lipstein was an American cinematographer known for his work on mid-20th-century Hollywood films.
-
D.
Allen Rivkin
Allen Rivkin was an American screenwriter active during Hollywood’s studio era, known for his work on numerous mid-20th-century films.
-
E.
Lawrence Benenson
Lawrence Benenson is an American real estate investor, philanthropist, and art collector known for his role in the Benenson family’s property business and support of cultural and educational institutions.
- 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_69e245b821008190b0e09cb02092aae1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183ea23088190b2f42d9bf01514ac |
completed | April 29, 2026, 4:07 a.m. |
Created at: April 17, 2026, 3:52 p.m.