Triple
T12918015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prelude in E-flat major, BWV 852 |
E309039
|
entity |
| Predicate | tuningSystemContext |
P65278
|
FINISHED |
| Object | well-tempered tuning |
—
|
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: well-tempered tuning | Statement: [Prelude in E-flat major, BWV 852, tuningSystemContext, well-tempered tuning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tuningSystemContext Context triple: [Prelude in E-flat major, BWV 852, tuningSystemContext, well-tempered tuning]
-
A.
tonalSystem
Indicates a relationship where a language or musical system is characterized by a specific set of tonal patterns or pitch distinctions that structure its sounds or expressions.
-
B.
usesMusicalSystem
chosen
Indicates that one entity employs or operates according to a particular musical system, framework, or set of musical rules.
-
C.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
D.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
E.
tuningOfInstrument
Indicates the specific tuning configuration or pitch arrangement applied to 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a1e8088190af697629baecf59f |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.