Triple
T20797897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lou Silver Harrison |
E511958
|
entity |
| Predicate | usedTuningSystem |
P141576
|
FINISHED |
| Object | just intonation |
—
|
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: just intonation | Statement: [Lou Silver Harrison, usedTuningSystem, just intonation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedTuningSystem Context triple: [Lou Silver Harrison, usedTuningSystem, just intonation]
-
A.
usesTuningReference
Indicates that one entity adopts another entity as the pitch or frequency standard for tuning.
-
B.
tuningType
Indicates the specific method or configuration by which something is adjusted or calibrated to achieve a desired performance or behavior.
-
C.
usesAutoTune
Indicates that the subject employs automatic pitch-correction technology (Auto-Tune) on their vocal or audio recordings.
-
D.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
E.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
- 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_69e0b4cc69f481908e98751e697b9df4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2ae2c4c819087f620df31dc1aba |
completed | April 21, 2026, 12:19 a.m. |
| PD | Predicate disambiguation | batch_69e5c0575b1c81908d010223fcd1213e |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3cbe5788190b7ace43bfdac2ef6 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:39 p.m.