Triple
T8483141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WaveRNN |
E200566
|
entity |
| Predicate | moreEfficientThan |
P83524
|
FINISHED |
| Object | WaveNet |
E39544
|
NE FINISHED |
How this triple was built (3 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: WaveNet | Statement: [WaveRNN, moreEfficientThan, WaveNet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WaveNet Context triple: [WaveRNN, moreEfficientThan, WaveNet]
-
A.
WaveNet
chosen
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
-
B.
Parallel WaveNet
Parallel WaveNet is a neural vocoder architecture that accelerates high-fidelity audio waveform generation by distilling the autoregressive WaveNet model into a fast, parallelizable form.
-
C.
WaveRNN
WaveRNN is a neural network-based audio waveform generator designed as a more efficient, real-time alternative to earlier autoregressive models for tasks like text-to-speech synthesis.
-
D.
Tacotron
Tacotron is a neural network-based text-to-speech system that generates natural-sounding speech by predicting mel-spectrograms from text, often used in conjunction with neural vocoders like Parallel WaveNet.
-
E.
WaveGlow
WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moreEfficientThan Context triple: [WaveRNN, moreEfficientThan, WaveNet]
-
A.
isLessEfficientThan
Indicates that one entity performs a task or uses resources with lower efficiency compared to another entity.
-
B.
maximumEfficiency
Indicates that an entity operates at its highest possible level of performance or productivity under given conditions.
-
C.
fasterThan
Indicates that one entity moves, operates, or progresses at a higher speed than another entity.
-
D.
moreExpensiveThan
Indicates that one entity has a higher cost or price than another entity.
-
E.
moreExpressiveThan
Indicates that one entity conveys ideas, emotions, or information with greater richness, nuance, or clarity than another.
- F. None of above. chosen
Provenance (5 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53845e881909eeb32863c7aa942 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebb5dbfc08190827e8e886a37a8be |
completed | April 2, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69cbd107633c8190a36ba50e07876918 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:12 p.m.