Triple

T8577263
Position Surface form Disambiguated ID Type / Status
Subject Parallel WaveNet E203077 entity
Predicate studentModel P82942 FINISHED
Object parallel WaveNet E203077 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: parallel WaveNet | Statement: [Parallel WaveNet, studentModel, parallel WaveNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: parallel WaveNet
Context triple: [Parallel WaveNet, studentModel, parallel WaveNet]
  • A. Parallel WaveNet chosen
    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.
  • 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. WaveGlow
    WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
  • E. Wav2Vec2
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • 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: studentModel
Context triple: [Parallel WaveNet, studentModel, parallel WaveNet]
  • A. enrollmentModel
    Indicates the type or structure of the enrollment relationship that governs how entities (such as users or participants) are registered or associated with a program, course, or service.
  • B. enteredStud
    Indicates that a student has enrolled in or begun attending a particular course, program, or institution.
  • C. studentOrAssistant
    Indicates that an individual has the role of either a student or an assistant in a given context or relationship.
  • D. studentsWing
    Indicates a relationship where a particular wing, section, or area is designated for or associated with students.
  • E. studentSection
    Indicates a relationship where a student is enrolled in or associated with a particular course section.
  • 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_69ca8328ebe481909a8c038fa79959b4 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbea97787481909ebbaa45f59cbdaa completed March 31, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce899dd7d48190b44338b92ad68bd0 completed April 2, 2026, 3:22 p.m.
PD Predicate disambiguation batch_69cbd11b13108190b07f8f161425a585 completed March 31, 2026, 1:50 p.m.
PDg Predicate description generation batch_69cbe12dd0b88190a38ec4d15dcc870b completed March 31, 2026, 2:58 p.m.
Created at: March 30, 2026, 6:22 p.m.