Triple
T1408250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deuces Wild |
E31745
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Paul Kimatian
Paul Kimatian is a screenwriter best known for his work on the gambling-themed film "Deuces Wild."
|
E163590
|
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: Paul Kimatian | Statement: [Deuces Wild, screenwriter, Paul Kimatian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Kimatian Context triple: [Deuces Wild, screenwriter, Paul Kimatian]
-
A.
Anthony Katagas
Anthony Katagas is an American film producer known for his work on acclaimed independent and studio films, including collaborations with prominent directors such as James Gray.
-
B.
Andrew Miano
Andrew Miano is an American film producer known for his work on independent and critically acclaimed movies, often collaborating with director Tom Ford and others.
-
C.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
D.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
E.
Kenyatta Matthews
Kenyatta Matthews is the wife of American author and journalist Ta-Nehisi Coates, known for her private life outside of his public literary career.
- 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: Paul Kimatian Triple: [Deuces Wild, screenwriter, Paul Kimatian]
Generated description
Paul Kimatian is a screenwriter best known for his work on the gambling-themed film "Deuces Wild."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paul Kimatian Target entity description: Paul Kimatian is a screenwriter best known for his work on the gambling-themed film "Deuces Wild."
-
A.
Anthony Katagas
Anthony Katagas is an American film producer known for his work on acclaimed independent and studio films, including collaborations with prominent directors such as James Gray.
-
B.
Andrew Miano
Andrew Miano is an American film producer known for his work on independent and critically acclaimed movies, often collaborating with director Tom Ford and others.
-
C.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
D.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
E.
Kenyatta Matthews
Kenyatta Matthews is the wife of American author and journalist Ta-Nehisi Coates, known for her private life outside of his public literary career.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3bf7f0c8190aee96818de6ff4a5 |
completed | March 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad015a52a88190933e7337067e5364 |
completed | March 8, 2026, 4:55 a.m. |
| NEDg | Description generation | batch_69ad01eb335c81908775e89b8a0348cc |
completed | March 8, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad029584308190af10520e928f890b |
completed | March 8, 2026, 5:01 a.m. |
Created at: March 1, 2026, 7:59 p.m.