Triple
T14177649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cooler |
E351373
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Arthur Coburn
Arthur Coburn is a film editor best known for his work on the movie "The Cooler."
|
E1088759
|
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: Arthur Coburn | Statement: [The Cooler, editedBy, Arthur Coburn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arthur Coburn Context triple: [The Cooler, editedBy, Arthur Coburn]
-
A.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on major Hollywood productions, including the action-comedy classic "Beverly Hills Cop."
-
B.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on the 1994 Jim Carrey comedy "The Mask."
-
C.
Warren William
Warren William was an American stage and film actor of the 1930s, best known for his suave, often morally ambiguous leading and supporting roles in Hollywood pre-Code dramas and mysteries.
-
D.
Charles Bickford
Charles Bickford was an American character actor known for his rugged screen presence and acclaimed supporting roles in numerous classic Hollywood films.
-
E.
Warner Baxter
Warner Baxter was an American film actor best known for his Academy Award–winning performance in the 1928 film "In Old Arizona" and for his roles in early sound-era Hollywood dramas and crime films.
- 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: Arthur Coburn Triple: [The Cooler, editedBy, Arthur Coburn]
Generated description
Arthur Coburn is a film editor best known for his work on the movie "The Cooler."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arthur Coburn Target entity description: Arthur Coburn is a film editor best known for his work on the movie "The Cooler."
-
A.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on major Hollywood productions, including the action-comedy classic "Beverly Hills Cop."
-
B.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on the 1994 Jim Carrey comedy "The Mask."
-
C.
Warren William
Warren William was an American stage and film actor of the 1930s, best known for his suave, often morally ambiguous leading and supporting roles in Hollywood pre-Code dramas and mysteries.
-
D.
Charles Bickford
Charles Bickford was an American character actor known for his rugged screen presence and acclaimed supporting roles in numerous classic Hollywood films.
-
E.
Warner Baxter
Warner Baxter was an American film actor best known for his Academy Award–winning performance in the 1928 film "In Old Arizona" and for his roles in early sound-era Hollywood dramas and crime films.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61c76e8081909994b95b631100e9 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd32497780819092e2d2ffe2a9dcaf |
completed | May 8, 2026, 12:46 a.m. |
| NEDg | Description generation | batch_69fd331562308190a0a2dfcc4a0d26a0 |
completed | May 8, 2026, 12:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd338f13dc8190b264534ed9a78cb5 |
completed | May 8, 2026, 12:51 a.m. |
Created at: April 10, 2026, 1:02 a.m.