Triple
T3759141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ella and Louis |
E82119
|
entity |
| Predicate | personnel |
P50783
|
FINISHED |
| Object | Ray Brown |
E83235
|
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: Ray Brown | Statement: [Ella and Louis, personnel, Ray Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Brown Context triple: [Ella and Louis, personnel, Ray Brown]
-
A.
Ray Brown
chosen
Ray Brown was an acclaimed American jazz double bassist known for his work with the Oscar Peterson Trio and collaborations with many leading jazz artists.
-
B.
Charles Lloyd
Charles Lloyd is an influential American jazz saxophonist, flutist, and composer known for his genre-blending style and acclaimed work since the 1960s.
-
C.
Jimmy Cobb
Jimmy Cobb was an American jazz drummer best known for his subtle, swinging work on Miles Davis’s landmark album "Kind of Blue."
-
D.
Roy Eldridge
Roy Eldridge was an influential American jazz trumpeter known for his fiery style and as a key link between swing and bebop.
-
E.
Teddy Dunn
Teddy Dunn is an American actor best known for playing Duncan Kane on the television series "Veronica Mars."
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc20b20819095fedf803aadc53a |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e5133ba48190a18ea170e3b9e1cd |
completed | March 14, 2026, 4:33 a.m. |
Created at: March 8, 2026, 3:35 p.m.