Triple
T1408252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deuces Wild |
E31745
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Michael Cerenzie
Michael Cerenzie is a film producer known for his work on independent and genre films in Hollywood.
|
E218037
|
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: Michael Cerenzie | Statement: [Deuces Wild, producer, Michael Cerenzie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Cerenzie Context triple: [Deuces Wild, producer, Michael Cerenzie]
-
A.
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.
-
B.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
-
C.
Dan Koretzky
Dan Koretzky is an American music industry figure best known as the co-founder and driving force behind the influential independent record label Drag City.
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
E.
Michael Gaeta
Michael Gaeta is a film producer best known for his work on genre movies, including the 2011 remake of the horror-comedy "Fright Night."
- 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: Michael Cerenzie Triple: [Deuces Wild, producer, Michael Cerenzie]
Generated description
Michael Cerenzie is a film producer known for his work on independent and genre films in Hollywood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Cerenzie Target entity description: Michael Cerenzie is a film producer known for his work on independent and genre films in Hollywood.
-
A.
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.
-
B.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
-
C.
Dan Koretzky
Dan Koretzky is an American music industry figure best known as the co-founder and driving force behind the influential independent record label Drag City.
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
E.
Michael Gaeta
Michael Gaeta is a film producer best known for his work on genre movies, including the 2011 remake of the horror-comedy "Fright Night."
- 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_69adfb8a58ec81908b2bb5c27283bafa |
completed | March 8, 2026, 10:43 p.m. |
| NEDg | Description generation | batch_69adfc6aa96c81909ae3cff6c7ab7f79 |
completed | March 8, 2026, 10:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adfcebbc808190a74f9082636bce11 |
completed | March 8, 2026, 10:49 p.m. |
Created at: March 1, 2026, 7:59 p.m.