Triple
T21472458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fletcher–Munson equal-loudness contours |
E529765
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | psychoacoustic model |
C44821
|
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: psychoacoustic model Context triple: [Fletcher–Munson equal-loudness contours, instanceOf, psychoacoustic model]
-
A.
lossless audio codec
A lossless audio codec is a method of encoding digital audio that compresses data without any loss of quality, allowing the original audio to be perfectly reconstructed during playback or decoding.
-
B.
audio coding tool
An audio coding tool is a software or hardware component that compresses, encodes, and decodes digital audio signals to efficiently store, transmit, and reproduce sound with minimal loss of quality.
-
C.
MPEG-4 audio profile
An MPEG-4 audio profile is a standardized set of audio coding tools and constraints within the MPEG-4 framework that defines the capabilities, complexity, and interoperability of encoded audio streams.
-
D.
speech codec
A speech codec is a system that encodes and compresses spoken audio into a digital format for efficient transmission or storage and then decodes it back into intelligible speech.
-
E.
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.
- 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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
Created at: April 16, 2026, 6:19 p.m.