Triple
T15625048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calendar Girls |
E375657
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Nick Barton
Nick Barton is a British film producer best known for his work on the hit comedy-drama film "Calendar Girls."
|
E1178213
|
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: Nick Barton | Statement: [Calendar Girls, producer, Nick Barton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick Barton Context triple: [Calendar Girls, producer, Nick Barton]
-
A.
Nick Barton
Nick Barton is a prominent evolutionary biologist known for his influential work on the genetics of adaptation and speciation.
-
B.
Sean Barton
Sean Barton is a film editor known for his work on various feature films, including the drama "Tea with Mussolini."
-
C.
Nick Bateman
Nick Bateman is a Canadian actor and model known for his roles in romantic films and his large social media following.
-
D.
Michael Barnathan
Michael Barnathan is an American film producer known for working on major studio hits such as the "Night at the Museum" series and the "Harry Potter" films.
-
E.
Chris Bacon
Chris Bacon is an American film and television composer known for scoring projects such as the psychological horror series "Bates Motel."
- 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: Nick Barton Triple: [Calendar Girls, producer, Nick Barton]
Generated description
Nick Barton is a British film producer best known for his work on the hit comedy-drama film "Calendar Girls."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nick Barton Target entity description: Nick Barton is a British film producer best known for his work on the hit comedy-drama film "Calendar Girls."
-
A.
Nick Barton
Nick Barton is a prominent evolutionary biologist known for his influential work on the genetics of adaptation and speciation.
-
B.
Sean Barton
Sean Barton is a film editor known for his work on various feature films, including the drama "Tea with Mussolini."
-
C.
Nick Bateman
Nick Bateman is a Canadian actor and model known for his roles in romantic films and his large social media following.
-
D.
Michael Barnathan
Michael Barnathan is an American film producer known for working on major studio hits such as the "Night at the Museum" series and the "Harry Potter" films.
-
E.
Chris Bacon
Chris Bacon is an American film and television composer known for scoring projects such as the psychological horror series "Bates Motel."
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9e5e248190ae54cda1fde51efb |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff997e13e4819080a39f59172ab99c |
completed | May 9, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69ff9a0fdf388190b2742e19f307c570 |
completed | May 9, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9a77d6d88190817158c30c56d70c |
completed | May 9, 2026, 8:35 p.m. |
Created at: April 10, 2026, 4:14 a.m.