Triple
T13486375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Whole Nine Yards |
E318514
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Allan Kaufman
Allan Kaufman is a film producer best known for his work on the crime-comedy movie "The Whole Nine Yards."
|
E1119393
|
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: Allan Kaufman | Statement: [The Whole Nine Yards, producer, Allan Kaufman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allan Kaufman Context triple: [The Whole Nine Yards, producer, Allan Kaufman]
-
A.
Allan Zunner
Allan Zunner is a mixed martial artist known for competing in the Ultimate Fighting Championship, including a bout on the UFC 81 fight card.
-
B.
Douglas Meyer
Douglas Meyer is a theatrical producer best known for his work on the Broadway musical adaptation of "The Wedding Singer."
-
C.
Charles Guggenheim
Charles Guggenheim was an American documentary filmmaker renowned for his politically engaged and historically focused films, earning multiple Academy Awards over his career.
-
D.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
E.
Mac Wellman
Mac Wellman is an American playwright and poet known for his experimental, language-driven theater and influential role in contemporary avant-garde drama.
- 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: Allan Kaufman Triple: [The Whole Nine Yards, producer, Allan Kaufman]
Generated description
Allan Kaufman is a film producer best known for his work on the crime-comedy movie "The Whole Nine Yards."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Allan Kaufman Target entity description: Allan Kaufman is a film producer best known for his work on the crime-comedy movie "The Whole Nine Yards."
-
A.
Allan Zunner
Allan Zunner is a mixed martial artist known for competing in the Ultimate Fighting Championship, including a bout on the UFC 81 fight card.
-
B.
Douglas Meyer
Douglas Meyer is a theatrical producer best known for his work on the Broadway musical adaptation of "The Wedding Singer."
-
C.
Charles Guggenheim
Charles Guggenheim was an American documentary filmmaker renowned for his politically engaged and historically focused films, earning multiple Academy Awards over his career.
-
D.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
E.
Mac Wellman
Mac Wellman is an American playwright and poet known for his experimental, language-driven theater and influential role in contemporary avant-garde drama.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3a15b48190b63fb59e926a97ae |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cce10c88190bf25404fc4c75bbf |
completed | May 8, 2026, 4:18 p.m. |
| NEDg | Description generation | batch_69fe1903c0f88190b6f1a081047506d5 |
completed | May 8, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe1980179481908b9f97e2f474e00d |
completed | May 8, 2026, 5:12 p.m. |
Created at: April 9, 2026, 9:42 p.m.