Triple
T23277807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beats Solo Buds |
E588770
|
entity |
| Predicate | audioTuning |
P119220
|
FINISHED |
| Object | bass-forward sound |
—
|
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: bass-forward sound | Statement: [Beats Solo Buds, audioTuning, bass-forward sound]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: audioTuning Context triple: [Beats Solo Buds, audioTuning, bass-forward sound]
-
A.
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.
-
B.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
C.
audioProcessing
Indicates that one entity performs operations to analyze, modify, or transform audio data associated with another entity.
-
D.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
E.
tuningType
chosen
Indicates the specific method or configuration by which something is adjusted or calibrated to achieve a desired performance or behavior.
- 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957991108190ac82fa6dd355f722 |
completed | April 29, 2026, 5:22 a.m. |
| PD | Predicate disambiguation | batch_69effcecabd88190856fb6e1d993e4dd |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:49 p.m.