Triple
T3760313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Princess Bride |
E82144
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Andrew Scheinman |
E463171
|
NE FINISHED |
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: Andrew Scheinman | Statement: [The Princess Bride, producer, Andrew Scheinman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrew Scheinman Context triple: [The Princess Bride, producer, Andrew Scheinman]
-
A.
Andrew Scheinman
chosen
Andrew Scheinman is an American film and television producer and director best known for his work on popular comedies such as "When Harry Met Sally..." and his collaborations with Rob Reiner.
-
B.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
C.
Jay O. Rothman
Jay O. Rothman is an American attorney and academic leader who serves as president of the University of Wisconsin System.
-
D.
Alan H. Fishman
Alan H. Fishman is an American banking executive best known for briefly serving as CEO of Washington Mutual during its 2008 financial collapse.
-
E.
Howard Rosenman
Howard Rosenman is an American film producer known for his work on popular Hollywood movies and for helping bring LGBTQ themes into mainstream cinema.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc3d3f48190974cec104080949f |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be395356e88190ba6c4b228669e40c |
completed | March 21, 2026, 6:23 a.m. |
Created at: March 8, 2026, 3:35 p.m.