Triple
T30465064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Art Baron |
E775112
|
entity |
| Predicate | musicSpecialty |
P143203
|
FINISHED |
| Object | big band trombone |
—
|
LITERAL 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: big band trombone | Statement: [Art Baron, musicSpecialty, big band trombone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: musicSpecialty Context triple: [Art Baron, musicSpecialty, big band trombone]
-
A.
genreSpecialty
Indicates that an entity specializes in or is particularly associated with a specific genre.
-
B.
hasMusicSpecialism
chosen
Indicates that an entity has a particular area of specialization or focus within the field of music.
-
C.
musicFeatured
Indicates that a particular piece of music is prominently included or used within another work, event, or context.
-
D.
musicMotif
Indicates a recurring musical idea, theme, or pattern that appears multiple times within a composition or across related works.
-
E.
musicField
Indicates a relationship where an entity is associated with a particular field, genre, or domain within music.
- F. None of above.
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_69f2249622a48190b1fae2e3e4ee958a |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: April 29, 2026, 8:11 p.m.