Triple
T5784541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Classics IV |
E128236
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object | Buddy Buie |
E67608
|
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: Buddy Buie | Statement: [Classics IV, associatedAct, Buddy Buie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buddy Buie Context triple: [Classics IV, associatedAct, Buddy Buie]
-
A.
Buddy Buie
chosen
Buddy Buie was an American songwriter and producer best known for crafting numerous hits in the 1960s and 1970s, particularly with the Atlanta Rhythm Section and Classics IV.
-
B.
Kim Boggs
Kim Boggs is the compassionate teenage girl who becomes Edward’s love interest and moral anchor in the fantasy film "Edward Scissorhands."
-
C.
Hank Booth
Hank Booth is a recurring character on the television series "Bones," known as Seeley Booth's grandfather and a former Army veteran.
-
D.
Bobby Dyer
Bobby Dyer is an American politician who serves as the mayor of Virginia Beach, Virginia.
-
E.
Buddy Baker
Buddy Baker was an American composer best known for his extensive work on Disney film scores and theme park attractions.
- 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_69c0084450048190bc647b649a05136b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a19870c819084dc68b36b0cdd60 |
completed | March 22, 2026, 5:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1134155608190bc9271dba417a512 |
completed | March 23, 2026, 10:17 a.m. |
Created at: March 22, 2026, 3:51 p.m.