Triple
T11049813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Come to Daddy |
E261215
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Daniel Bekerman
Daniel Bekerman is a Canadian film producer known for working on independent and genre films, including the horror-comedy thriller "Come to Daddy."
|
E901301
|
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: Daniel Bekerman | Statement: [Come to Daddy, producer, Daniel Bekerman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Bekerman Context triple: [Come to Daddy, producer, Daniel Bekerman]
-
A.
Ben Dunkelman
Ben Dunkelman was a Canadian-born Jewish officer and businessman best known for his prominent command role in Israel’s 1948 War of Independence.
-
B.
Daniel Matmor
Daniel Matmor is an actor best known for his role in the 1995 horror film "The Mangler," adapted from a Stephen King short story.
-
C.
Daniel von Bargen
Daniel von Bargen was an American character actor known for his authoritative and often villainous roles in film and television, including appearances in projects like "The General’s Daughter," "Seinfeld," and "Malcolm in the Middle."
-
D.
David Ortkiese
David Ortkiese is a cinematographer known for his work on the film "A Haunted House."
-
E.
Daniel Zelman
Daniel Zelman is an American actor, screenwriter, and television producer known for co-creating the legal thriller series "Damages."
- 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: Daniel Bekerman Triple: [Come to Daddy, producer, Daniel Bekerman]
Generated description
Daniel Bekerman is a Canadian film producer known for working on independent and genre films, including the horror-comedy thriller "Come to Daddy."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Bekerman Target entity description: Daniel Bekerman is a Canadian film producer known for working on independent and genre films, including the horror-comedy thriller "Come to Daddy."
-
A.
Ben Dunkelman
Ben Dunkelman was a Canadian-born Jewish officer and businessman best known for his prominent command role in Israel’s 1948 War of Independence.
-
B.
Daniel Matmor
Daniel Matmor is an actor best known for his role in the 1995 horror film "The Mangler," adapted from a Stephen King short story.
-
C.
Daniel von Bargen
Daniel von Bargen was an American character actor known for his authoritative and often villainous roles in film and television, including appearances in projects like "The General’s Daughter," "Seinfeld," and "Malcolm in the Middle."
-
D.
David Ortkiese
David Ortkiese is a cinematographer known for his work on the film "A Haunted House."
-
E.
Daniel Zelman
Daniel Zelman is an American actor, screenwriter, and television producer known for co-creating the legal thriller series "Damages."
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79868c78881908c8e3672c05ae7ec |
completed | April 9, 2026, 12:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3aa06bae08190a0db615a258ded29 |
completed | April 18, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_69e3ad0379888190b2f56d36d79bf97d |
completed | April 18, 2026, 4:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b206c7a4819087eb06faa6e1af21 |
completed | April 18, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:26 p.m.