Triple
T3236974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Visit |
E67877
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Marc Bienstock
Marc Bienstock is a film producer best known for his work on horror and thriller movies, including collaborations with director M. Night Shyamalan.
|
E378869
|
NE FINISHED |
How this triple was built (4 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: Marc Bienstock | Statement: [The Visit, producer, Marc Bienstock]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marc Bienstock Context triple: [The Visit, producer, Marc Bienstock]
-
A.
Jay Bienstock
Jay Bienstock is a television producer best known for his work on major reality competition series, including serving as an executive producer on "The Apprentice."
-
B.
Marc Roskin
Marc Roskin is a television producer and director best known for his work on genre and adventure series such as "The Librarians."
-
C.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
-
D.
Jay Rabinowitz
Jay Rabinowitz is a film editor known for his work on numerous feature films, including the science-fiction thriller "The Adjustment Bureau."
-
E.
Guy Rothblum
Guy Rothblum is a theoretical computer scientist known for his work in cryptography and complexity theory.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Marc Bienstock Triple: [The Visit, producer, Marc Bienstock]
Generated description
Marc Bienstock is a film producer best known for his work on horror and thriller movies, including collaborations with director M. Night Shyamalan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marc Bienstock Target entity description: Marc Bienstock is a film producer best known for his work on horror and thriller movies, including collaborations with director M. Night Shyamalan.
-
A.
Jay Bienstock
Jay Bienstock is a television producer best known for his work on major reality competition series, including serving as an executive producer on "The Apprentice."
-
B.
Marc Roskin
Marc Roskin is a television producer and director best known for his work on genre and adventure series such as "The Librarians."
-
C.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
-
D.
Jay Rabinowitz
Jay Rabinowitz is a film editor known for his work on numerous feature films, including the science-fiction thriller "The Adjustment Bureau."
-
E.
Guy Rothblum
Guy Rothblum is a theoretical computer scientist known for his work in cryptography and complexity theory.
- F. None of above. chosen
Provenance (5 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef29bf48190a9aa3a39f0138428 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3601894819082568a7ee8d6aabc |
completed | March 14, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_69b4c3f108d481908e8c94feed20cfef |
completed | March 14, 2026, 2:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4c452904c8190a6de223594689e61 |
completed | March 14, 2026, 2:13 a.m. |
Created at: March 8, 2026, 3:08 p.m.