Triple
T22483596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G major |
E555827
|
entity |
| Predicate | keySignatureAccidentals |
P125129
|
FINISHED |
| Object | 1 sharp |
—
|
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: 1 sharp | Statement: [G major, keySignatureAccidentals, 1 sharp]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: keySignatureAccidentals Context triple: [G major, keySignatureAccidentals, 1 sharp]
-
A.
numberOfAccidentals
chosen
Indicates the relationship that specifies how many accidentals (sharps, flats, or naturals) are associated with a given musical element.
-
B.
musicalSymbol
Indicates that one entity is a musical notation mark or sign associated with another entity in a musical context.
-
C.
hasKeySignatureDistribution
Indicates that there is a specific pattern or spread of key signatures associated with the entity, describing how frequently or prominently different key signatures occur.
-
D.
hasKeySignature
Indicates that one musical work, passage, or notation is associated with a specific key signature defining its set of sharps or flats.
-
E.
musicTheoryContext
Indicates that one entity is interpreted or analyzed within the framework, rules, or concepts of music theory in relation to another entity or situation.
- 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_69e11e53897c819088863779f8c50bb0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15c3b03d881909e286a124c1a2b1c |
completed | April 29, 2026, 1:17 a.m. |
| PD | Predicate disambiguation | batch_69e898b6eee08190ba673a0ee329e671 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:49 p.m.