Triple
T28997710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | INTERSPEECH |
E736212
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | speech and language technology conference |
C29000
|
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: speech and language technology conference Context triple: [INTERSPEECH, instanceOf, speech and language technology conference]
-
A.
natural language processing conference
chosen
A natural language processing conference is a formal gathering where researchers, practitioners, and industry professionals present, discuss, and advance methods and applications for computational understanding and generation of human language.
-
B.
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.
-
C.
linguistics conference
A linguistics conference is a formal gathering where researchers, educators, and practitioners present, discuss, and critique work on language and its structure, use, and acquisition.
-
D.
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.
-
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.
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_69f077eacd0481908ef0bafd74491cd0 |
completed | April 28, 2026, 9:03 a.m. |
Created at: April 28, 2026, 9:32 a.m.