Triple

T19505256
Position Surface form Disambiguated ID Type / Status
Subject Shadow Monster E488004 entity
Predicate hasLanguage P15 FINISHED
Object Deep Speech — NE NERFINISHED

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: Deep Speech | Statement: [Shadow Monster, hasLanguage, Deep Speech]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Deep Speech
Context triple: [Shadow Monster, hasLanguage, Deep Speech]
  • A. 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.
  • B. WaveNet
    WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
  • 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. Wav2Vec2
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • E. WavLM
    WavLM is a self-supervised speech representation model developed by Microsoft that extends wav2vec-style architectures to better handle noisy, multi-speaker, and diverse speech scenarios for tasks like recognition and speaker diarization.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Deep Speech
Target entity description: Deep Speech is the eerie, alien language associated with aberrant and otherworldly creatures in fantasy role-playing settings.
  • A. 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.
  • B. WaveNet
    WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
  • 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. Wav2Vec2
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • E. WavLM
    WavLM is a self-supervised speech representation model developed by Microsoft that extends wav2vec-style architectures to better handle noisy, multi-speaker, and diverse speech scenarios for tasks like recognition and speaker diarization.
  • F. None of above. chosen

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_69d8e8d9d1c88190b01cd78b8be49384 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e635113fdc819098ea0f738d01925c completed April 20, 2026, 2:15 p.m.
Created at: April 10, 2026, 1:40 p.m.