Triple
T13933494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2 Guns |
E335049
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Adam Siegel
Adam Siegel is a film producer known for working on major Hollywood action and crime movies, including the 2013 film "2 Guns."
|
E1152235
|
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: Adam Siegel | Statement: [2 Guns, producer, Adam Siegel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adam Siegel Context triple: [2 Guns, producer, Adam Siegel]
-
A.
Adam Siegel
Adam Siegel is a film producer known for his work on action and genre movies, including the 2008 thriller "Wanted."
-
B.
Ian Siegel
Ian Siegel is an American entrepreneur best known as the co-founder and longtime CEO of the online employment marketplace ZipRecruiter.
-
C.
Josh Kesselman
Josh Kesselman is a film and television producer best known for his work as an executive producer on projects such as the series "The Great."
-
D.
Neil Siegel
Neil Siegel is a prominent American legal scholar known for his work in constitutional law and theory, including the study of judicial behavior and the separation of powers.
-
E.
Matthew Salsberg
Matthew Salsberg is a television writer and producer best known for his work on the dark comedy series "Weeds."
- 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: Adam Siegel Triple: [2 Guns, producer, Adam Siegel]
Generated description
Adam Siegel is a film producer known for working on major Hollywood action and crime movies, including the 2013 film "2 Guns."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Adam Siegel Target entity description: Adam Siegel is a film producer known for working on major Hollywood action and crime movies, including the 2013 film "2 Guns."
-
A.
Adam Siegel
Adam Siegel is a film producer known for his work on action and genre movies, including the 2008 thriller "Wanted."
-
B.
Ian Siegel
Ian Siegel is an American entrepreneur best known as the co-founder and longtime CEO of the online employment marketplace ZipRecruiter.
-
C.
Josh Kesselman
Josh Kesselman is a film and television producer best known for his work as an executive producer on projects such as the series "The Great."
-
D.
Neil Siegel
Neil Siegel is a prominent American legal scholar known for his work in constitutional law and theory, including the study of judicial behavior and the separation of powers.
-
E.
Matthew Salsberg
Matthew Salsberg is a television writer and producer best known for his work on the dark comedy series "Weeds."
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2cf28df081908d897d7b9ec7939d |
completed | April 14, 2026, 12:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01d196d88190a8fa54468b2de1bb |
completed | May 9, 2026, 9:43 a.m. |
| NEDg | Description generation | batch_69ff02c10b648190b1e2e04aa0c2596d |
completed | May 9, 2026, 9:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff066e367c8190a0720fe636355ff8 |
completed | May 9, 2026, 10:03 a.m. |
Created at: April 9, 2026, 10:17 p.m.