Triple
T20324075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Duryea |
E492286
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Daniel Duryea |
—
|
NE NERFINISHED |
How this triple was built (2 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: Daniel Duryea | Statement: [Dan Duryea, birthName, Daniel Duryea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Duryea Context triple: [Dan Duryea, birthName, Daniel Duryea]
-
A.
Dan Duryea
chosen
Dan Duryea was an American character actor best known for his distinctive portrayals of sneering villains and tough guys in film noir and classic Hollywood movies of the 1940s and 1950s.
-
B.
Robert Hoyt
Robert Hoyt is an individual notable enough to be recognized as a prominent bearer of the Hoyt surname.
-
C.
Harry Davenport
Harry Davenport was an American character actor best known for his numerous supporting roles in classic Hollywood films of the 1930s and 1940s.
-
D.
Boyd Holbrook
Boyd Holbrook is an American actor and former model known for roles in films like "Logan" and "Gone Girl" and the Netflix series "Narcos."
-
E.
Edward Van Sloan
Edward Van Sloan was an American character actor best known for his roles in early Universal horror films, including memorable appearances in classics like Dracula and Frankenstein.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a0134081909113563e1c3ba68a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6778e59508190bfd7a3ce44d56a93 |
completed | April 20, 2026, 6:59 p.m. |
Created at: April 16, 2026, 11:21 a.m.