Triple
T15355231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office Space |
E367154
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Daniel Rappaport |
—
|
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: Daniel Rappaport | Statement: [Office Space, producer, Daniel Rappaport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Rappaport Context triple: [Office Space, producer, Daniel Rappaport]
-
A.
Daniel Rappaport
chosen
Daniel Rappaport is a film producer known for working on mainstream Hollywood comedies, including the movie "Office Christmas Party."
-
B.
David Rappaport
David Rappaport was a British actor best known for his roles in fantasy and science-fiction films and television series during the late 20th century.
-
C.
Andrew Rabinovich
Andrew Rabinovich is a computer scientist and researcher known for his contributions to computer vision and deep learning, including influential work at Google.
-
D.
Michael Nudelman
Michael Nudelman was an Israeli politician and Knesset member known for representing Russian-speaking immigrants and serving in several immigrant-focused political parties.
-
E.
Michael Rothschild
Michael Rothschild is an economist known for his influential work in information economics and decision theory, and for mentoring prominent scholars such as Nobel laureate Oliver Hart.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2c00648190ae2325e1ee58dcfd |
completed | April 16, 2026, 1:41 a.m. |
Created at: April 10, 2026, 3:18 a.m.