Triple
T8520543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blumenstück in D-flat major, Op. 19 |
E201682
|
entity |
| Predicate | hasPianoTechniqueFocus |
P70900
|
FINISHED |
| Object | legato tone production |
—
|
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: legato tone production | Statement: [Blumenstück in D-flat major, Op. 19, hasPianoTechniqueFocus, legato tone production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPianoTechniqueFocus Context triple: [Blumenstück in D-flat major, Op. 19, hasPianoTechniqueFocus, legato tone production]
-
A.
hasTechnique
Indicates that an entity employs, utilizes, or is associated with a particular method, procedure, or technique.
-
B.
notableInstrumentTechnique
chosen
Indicates that an entity is particularly recognized for using, developing, or being associated with a specific musical instrument technique.
-
C.
hasPianos
Indicates that one entity possesses, contains, or includes one or more pianos in relation to another entity.
-
D.
beganPlayingPianoAtAge
Indicates the age at which an individual first started playing the piano.
-
E.
teachesInstrument
Indicates that one entity provides instruction or lessons to another entity on how to play a musical instrument.
- 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe628f8c48190a35201f9fde605cc |
completed | March 31, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cbd10f64b4819080859057c19e58f0 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:16 p.m.