Triple
T33283784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chopin piano works recordings |
E852122
|
entity |
| Predicate | usesTuningStandard |
P141576
|
FINISHED |
| Object | equal temperament |
—
|
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: equal temperament | Statement: [Chopin piano works recordings, usesTuningStandard, equal temperament]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTuningStandard Context triple: [Chopin piano works recordings, usesTuningStandard, equal temperament]
-
A.
usesTuningReference
Indicates that one entity adopts another entity as the pitch or frequency standard for tuning.
-
B.
usedTuningSystem
chosen
Indicates that an entity (such as a musical work, performance, or instrument) employs or is based on a particular tuning system.
-
C.
tuningType
Indicates the specific method or configuration by which something is adjusted or calibrated to achieve a desired performance or behavior.
-
D.
usesAutoTune
Indicates that the subject employs automatic pitch-correction technology (Auto-Tune) on their vocal or audio recordings.
-
E.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
- 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_69f349660ff48190a4568803d0b89941 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 1, 2026, 1:32 a.m.