Triple

T18205214
Position Surface form Disambiguated ID Type / Status
Subject HuBERT E435884 entity
Predicate introducedInPaper P513 FINISHED
Object HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units — NE NERFINISHED

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: HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units | Statement: [HuBERT, introducedInPaper, HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Context triple: [HuBERT, introducedInPaper, HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units]
  • A. HuBERT chosen
    HuBERT is a self-supervised speech representation learning model that learns powerful audio features from unlabeled speech for tasks like automatic speech recognition and audio classification.
  • B. Wav2Vec2
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • C. 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.
  • D. WaveNet
    WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e222831081908f7d5500424e3acb completed April 19, 2026, 2:09 p.m.
Created at: April 10, 2026, 10:32 a.m.