Triple
T10588670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanna (2011 film) |
E249924
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Scott Nemes
Scott Nemes is a film producer best known for his work on the 2011 drama "Hanna."
|
E872113
|
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: Scott Nemes | Statement: [Hanna (2011 film), producer, Scott Nemes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Nemes Context triple: [Hanna (2011 film), producer, Scott Nemes]
-
A.
Stephen Nemeth
Stephen Nemeth is an American film producer known for his work on independent and cult films, including the adaptation of Hunter S. Thompson’s "Fear and Loathing in Las Vegas."
-
B.
Bill Neukom
Bill Neukom is an American lawyer and philanthropist best known as Microsoft’s former chief legal officer and a former managing general partner of the San Francisco Giants.
-
C.
Nick Fazekas
Nick Fazekas is an American former professional basketball player best known as a dominant scoring and rebounding forward at the University of Nevada before playing in the NBA and overseas.
-
D.
Michael Nolin
Michael Nolin is an American film producer best known for his work on the acclaimed music drama "Mr. Holland's Opus."
-
E.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
- 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: Scott Nemes Triple: [Hanna (2011 film), producer, Scott Nemes]
Generated description
Scott Nemes is a film producer best known for his work on the 2011 drama "Hanna."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Scott Nemes Target entity description: Scott Nemes is a film producer best known for his work on the 2011 drama "Hanna."
-
A.
Stephen Nemeth
Stephen Nemeth is an American film producer known for his work on independent and cult films, including the adaptation of Hunter S. Thompson’s "Fear and Loathing in Las Vegas."
-
B.
Bill Neukom
Bill Neukom is an American lawyer and philanthropist best known as Microsoft’s former chief legal officer and a former managing general partner of the San Francisco Giants.
-
C.
Nick Fazekas
Nick Fazekas is an American former professional basketball player best known as a dominant scoring and rebounding forward at the University of Nevada before playing in the NBA and overseas.
-
D.
Michael Nolin
Michael Nolin is an American film producer best known for his work on the acclaimed music drama "Mr. Holland's Opus."
-
E.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d527793c588190bfe3a5261eb7f919 |
completed | April 7, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b9440548190bff01847a940266b |
completed | April 10, 2026, 7:12 p.m. |
| NEDg | Description generation | batch_69d94ca13550819085b7824d8b5131e7 |
completed | April 10, 2026, 7:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9518517608190b5036694b83f5f58 |
completed | April 10, 2026, 7:37 p.m. |
Created at: April 6, 2026, 12:40 p.m.