Triple
T21405368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holiday in the Wild |
E528017
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Charles Norris
Charles Norris is a film editor known for his work on the romantic drama movie "Holiday in the Wild."
|
E1482863
|
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: Charles Norris | Statement: [Holiday in the Wild, editor, Charles Norris]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charles Norris Context triple: [Holiday in the Wild, editor, Charles Norris]
-
A.
Frank S. Black
Frank S. Black was a Republican politician who served as the 32nd governor of New York from 1897 to 1898.
-
B.
Will Gardner
Will Gardner is a charismatic and ambitious lawyer and name partner at the Chicago law firm Lockhart/Gardner in the television drama "The Good Wife."
-
C.
Robert Coddington
Robert Coddington is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the Coddington surname.
-
D.
John Garrison
John Garrison is a British singer-songwriter and musician known for his solo work and for contributing bass and backing vocals to James Blunt’s music.
-
E.
John Bridger
John Bridger is a veteran safecracker and mentor figure in the 2003 heist film "The Italian Job," known for planning intricate robberies and leading a close-knit crew.
- 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: Charles Norris Triple: [Holiday in the Wild, editor, Charles Norris]
Generated description
Charles Norris is a film editor known for his work on the romantic drama movie "Holiday in the Wild."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Charles Norris Target entity description: Charles Norris is a film editor known for his work on the romantic drama movie "Holiday in the Wild."
-
A.
Frank S. Black
Frank S. Black was a Republican politician who served as the 32nd governor of New York from 1897 to 1898.
-
B.
Will Gardner
Will Gardner is a charismatic and ambitious lawyer and name partner at the Chicago law firm Lockhart/Gardner in the television drama "The Good Wife."
-
C.
Robert Coddington
Robert Coddington is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the Coddington surname.
-
D.
John Garrison
John Garrison is a British singer-songwriter and musician known for his solo work and for contributing bass and backing vocals to James Blunt’s music.
-
E.
John Bridger
John Bridger is a veteran safecracker and mentor figure in the 2003 heist film "The Italian Job," known for planning intricate robberies and leading a close-knit crew.
- 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_69e0b520ee3c8190abddbee7e37e834c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b1aea22881909a0cc754e417fbf3 |
completed | April 22, 2026, 11:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09bb559d248190bef01cb86020d29c |
completed | May 17, 2026, 12:57 p.m. |
| NEDg | Description generation | batch_6a09bea4a674819093d5bfb9e6fe69b9 |
completed | May 17, 2026, 1:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09bf9d2d508190a45847afa0d47337 |
completed | May 17, 2026, 1:16 p.m. |
Created at: April 16, 2026, 5:31 p.m.