Triple
T14594523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Payday |
E342523
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Don Shebib
Don Shebib is a Canadian film director, producer, and editor best known for his influential 1970 drama "Goin' Down the Road," a landmark of Canadian cinema.
|
E1142682
|
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: Don Shebib | Statement: [Payday, producer, Don Shebib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Shebib Context triple: [Payday, producer, Don Shebib]
-
A.
Ted Shebib
Ted Shebib is the father of Canadian record producer and songwriter Noah "40" Shebib.
-
B.
Dan Futterman
Dan Futterman is an American actor and Academy Award–nominated screenwriter known for writing the film "Capote" and co-creating the TV series "In Treatment."
-
C.
Dan Charnas
Dan Charnas is an American author, journalist, and music industry veteran best known for his influential writings on hip-hop history and culture.
-
D.
Dan Weinreb
Dan Weinreb was an American computer scientist and software engineer best known for his pioneering work on Lisp machines and contributions to the Symbolics company and the broader Lisp community.
-
E.
Alan Siegel
Alan Siegel is a film producer best known for his long-running collaboration with actor Gerard Butler on action and thriller movies.
- 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: Don Shebib Triple: [Payday, producer, Don Shebib]
Generated description
Don Shebib is a Canadian film director, producer, and editor best known for his influential 1970 drama "Goin' Down the Road," a landmark of Canadian cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Don Shebib Target entity description: Don Shebib is a Canadian film director, producer, and editor best known for his influential 1970 drama "Goin' Down the Road," a landmark of Canadian cinema.
-
A.
Ted Shebib
Ted Shebib is the father of Canadian record producer and songwriter Noah "40" Shebib.
-
B.
Dan Futterman
Dan Futterman is an American actor and Academy Award–nominated screenwriter known for writing the film "Capote" and co-creating the TV series "In Treatment."
-
C.
Dan Charnas
Dan Charnas is an American author, journalist, and music industry veteran best known for his influential writings on hip-hop history and culture.
-
D.
Dan Weinreb
Dan Weinreb was an American computer scientist and software engineer best known for his pioneering work on Lisp machines and contributions to the Symbolics company and the broader Lisp community.
-
E.
Alan Siegel
Alan Siegel is a film producer best known for his long-running collaboration with actor Gerard Butler on action and thriller movies.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43480d8819084a707e56da2c237 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed318aa908190a59b0def01a9cb16 |
completed | May 9, 2026, 6:24 a.m. |
| NEDg | Description generation | batch_69fed3d50d688190a05f8245142edbe4 |
completed | May 9, 2026, 6:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed42af9bc81909655adf2056f3bf7 |
completed | May 9, 2026, 6:28 a.m. |
Created at: April 10, 2026, 1:24 a.m.