Triple
T37429384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acoustic theory of speech production |
E930089
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | acoustic model |
C63253
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: acoustic model Context triple: [Acoustic theory of speech production, instanceOf, acoustic model]
-
A.
psychoacoustic model
A psychoacoustic model is a conceptual framework that predicts how humans perceive sound by simulating auditory system characteristics such as masking, loudness, and frequency resolution.
-
B.
speech foundation model
A speech foundation model is a large-scale, pre-trained neural network designed to understand, generate, and transform spoken language across diverse tasks, languages, and acoustic conditions.
-
C.
Fairlight CMI model
A Fairlight CMI model is a conceptual representation of the pioneering digital sampling synthesizer system, encapsulating its hardware components, sound sampling and synthesis capabilities, user interface, and role in music production workflows.
-
D.
self-supervised speech representation learning model
A self-supervised speech representation learning model is a neural network that learns meaningful audio and speech feature representations directly from large amounts of unlabeled speech data by solving pretext tasks such as masked prediction or contrastive learning.
-
E.
automatic speech recognition system
An automatic speech recognition system converts spoken language into written text by analyzing and interpreting audio signals using acoustic, linguistic, and statistical models.
- F. None of above. chosen
Provenance (1 batch)
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_69f76ebf0f288190ba198a78341613b8 |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:16 p.m.