Triple
T10316603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yankee Doodle Dandy |
E242032
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Robert Buckner |
E501813
|
NE FINISHED |
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: Robert Buckner | Statement: [Yankee Doodle Dandy, screenwriter, Robert Buckner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Buckner Context triple: [Yankee Doodle Dandy, screenwriter, Robert Buckner]
-
A.
Robert Buckner
chosen
Robert Buckner was an American screenwriter and film producer known for his work on classic Hollywood films in the 1930s and 1940s.
-
B.
Manuel Buckner
Manuel Buckner is an individual notable enough to be specifically cited as a bearer of the surname Buckner.
-
C.
Branford Buckner
Branford Buckner is a former American football defensive tackle who played in the NFL and later became a defensive line coach.
-
D.
Ralston Crawford
Ralston Crawford was an American painter and photographer known for his crisp, geometric depictions of industrial and urban scenes that made him a key figure in the Precisionist movement.
-
E.
Joseph Burkett
Joseph Burkett is an American defense contractor best known as the husband of journalist and war correspondent Lara Logan.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d35cf8cc819084dd472f22d604be |
completed | April 7, 2026, 9:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d65fc0948190af4356fc9f5004bb |
completed | April 18, 2026, 12:54 a.m. |
Created at: April 6, 2026, 11:49 a.m.