Triple
T1275844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cetera corsa |
E27210
|
entity |
| Predicate | tuningMethod |
P28297
|
FINISHED |
| Object | course-based 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: course-based tuning | Statement: [Cetera corsa, tuningMethod, course-based tuning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tuningMethod Context triple: [Cetera corsa, tuningMethod, course-based tuning]
-
A.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
B.
usesModulation
Indicates that one entity applies or employs a particular modulation method or scheme in relation to another entity or process.
-
C.
soundReproductionMethod
Indicates the method or technique used to reproduce or play back sound.
-
D.
musicalMode
Indicates the specific tonal framework or scale system (mode) in which a piece of music or musical passage is organized.
-
E.
audioModulation
Indicates a relationship where one audio signal or parameter is used to vary or control another audio signal’s characteristics (such as amplitude, frequency, or timbre) over time.
- F. None of above. chosen
Provenance (4 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_69a496d3710c8190955dee8bc0dacb50 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c31602b8819087a57e8d390cae7a |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4bee0be808190a8ccac6a41851fdd |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c31400c08190a07ccb736df3bf54 |
completed | March 1, 2026, 10:52 p.m. |
Created at: March 1, 2026, 7:50 p.m.