Triple
T3783753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battleship |
E85477
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Bennett Schneir
Bennett Schneir is a film producer best known for his work on large-scale Hollywood action and science fiction movies.
|
E420485
|
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: Bennett Schneir | Statement: [Battleship, producer, Bennett Schneir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bennett Schneir Context triple: [Battleship, producer, Bennett Schneir]
-
A.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
B.
Michael Shvo
Michael Shvo is a high-profile real estate developer and art collector known for leading luxury property projects in major global cities.
-
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.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
- 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: Bennett Schneir Triple: [Battleship, producer, Bennett Schneir]
Generated description
Bennett Schneir is a film producer best known for his work on large-scale Hollywood action and science fiction movies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bennett Schneir Target entity description: Bennett Schneir is a film producer best known for his work on large-scale Hollywood action and science fiction movies.
-
A.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
B.
Michael Shvo
Michael Shvo is a high-profile real estate developer and art collector known for leading luxury property projects in major global cities.
-
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.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee3dc6590819098dda08206206612 |
completed | March 9, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589bcbce88190b97b9dcec8a976f4 |
completed | March 14, 2026, 4:15 p.m. |
| NEDg | Description generation | batch_69b58ad68af88190863ea23e170ce2ca |
completed | March 14, 2026, 4:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b58ba2edbc81908bfbe5d91daf5ae5 |
completed | March 14, 2026, 4:24 p.m. |
Created at: March 9, 2026, 3:13 p.m.