Triple
T10682387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cake (2014 film) |
E251789
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Ben Barnz
Ben Barnz is an American film producer known for his work on independent dramas and character-driven films, including the 2014 movie "Cake."
|
E881480
|
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: Ben Barnz | Statement: [Cake (2014 film), producer, Ben Barnz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ben Barnz Context triple: [Cake (2014 film), producer, Ben Barnz]
-
A.
Daniel Barnz
Daniel Barnz is an American film director and screenwriter known for character-driven dramas such as "Cake" and "Won't Back Down."
-
B.
Greg Barnett
Greg Barnett is an actor known for his role in the 2013 television miniseries "The Bible."
-
C.
Jim Barnhill
Jim Barnhill was an American football official best known for serving as a referee in the American Football League during the 1960s.
-
D.
Todd Bunzl
Todd Bunzl, better known as Todd Phillips, is an American film director, producer, and screenwriter recognized for hit comedies like The Hangover trilogy and the dark thriller Joker.
-
E.
Dave Bannion
Dave Bannion is a tough, morally driven police detective who wages a relentless one-man war against corruption and organized crime in the classic film noir "The Big Heat."
- 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: Ben Barnz Triple: [Cake (2014 film), producer, Ben Barnz]
Generated description
Ben Barnz is an American film producer known for his work on independent dramas and character-driven films, including the 2014 movie "Cake."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ben Barnz Target entity description: Ben Barnz is an American film producer known for his work on independent dramas and character-driven films, including the 2014 movie "Cake."
-
A.
Daniel Barnz
chosen
Daniel Barnz is an American film director and screenwriter known for character-driven dramas such as "Cake" and "Won't Back Down."
-
B.
Greg Barnett
Greg Barnett is an actor known for his role in the 2013 television miniseries "The Bible."
-
C.
Jim Barnhill
Jim Barnhill was an American football official best known for serving as a referee in the American Football League during the 1960s.
-
D.
Todd Bunzl
Todd Bunzl, better known as Todd Phillips, is an American film director, producer, and screenwriter recognized for hit comedies like The Hangover trilogy and the dark thriller Joker.
-
E.
Dave Bannion
Dave Bannion is a tough, morally driven police detective who wages a relentless one-man war against corruption and organized crime in the classic film noir "The Big Heat."
- F. None of above.
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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fcc30be481909922844b539b622d |
completed | April 9, 2026, 1:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbb6fa3f54819081910a2589ddbc99 |
completed | April 12, 2026, 3:15 p.m. |
| NEDg | Description generation | batch_69dbbbe3d9dc819088f85d41ef66ab29 |
completed | April 12, 2026, 3:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69dbc58a5ef481908e67fff6686fb506 |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 8, 2026, 9:10 p.m.