Triple
T24077529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musitron |
E596409
|
entity |
| Predicate | tuningRange |
P154766
|
FINISHED |
| Object | high register focus |
—
|
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: high register focus | Statement: [Musitron, tuningRange, high register focus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tuningRange Context triple: [Musitron, tuningRange, high register focus]
-
A.
tuningType
Indicates the specific method or configuration by which something is adjusted or calibrated to achieve a desired performance or behavior.
-
B.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
C.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
D.
tuningDivision
Indicates a relationship where one entity specifies or defines the division or partitioning scheme used to tune or configure another entity.
-
E.
tunedOrUntuned
Indicates whether something is in a properly adjusted or calibrated state (tuned) or not (untuned).
- 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_69e288c3999c8190809b282a04813dec |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1db1e959c81909f4365b5d7f934d9 |
completed | April 29, 2026, 10:19 a.m. |
| PD | Predicate disambiguation | batch_69f1764b1d4c8190b12590c6339c31c1 |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f1785afe3c81909be28986ffe944bf |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 10:43 p.m.