Triple

T18205171
Position Surface form Disambiguated ID Type / Status
Subject Wav2Vec2 E435883 entity
Predicate hasVariant P455 FINISHED
Object XLSR-53 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: XLSR-53 | Statement: [Wav2Vec2, hasVariant, XLSR-53]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: XLSR-53
Context triple: [Wav2Vec2, hasVariant, XLSR-53]
  • A. Wav2Vec2
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • B. XLNet
    XLNet is a generalized autoregressive pretraining model for natural language processing that improves on BERT by leveraging permutation-based language modeling to better capture bidirectional context.
  • C. LM-5B
    LM-5B is a heavy-lift variant of China’s Long March 5 rocket family, primarily used to launch large modules for the Tiangong space station into low Earth orbit.
  • D. HuBERT
    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.
  • E. Transformer-XL
    Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
  • 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: XLSR-53
Target entity description: XLSR-53 is a multilingual variant of Facebook AI’s wav2vec 2.0 model trained on speech from dozens of languages to enable cross-lingual speech recognition and representation learning.
  • A. Wav2Vec2 chosen
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • B. XLNet
    XLNet is a generalized autoregressive pretraining model for natural language processing that improves on BERT by leveraging permutation-based language modeling to better capture bidirectional context.
  • C. LM-5B
    LM-5B is a heavy-lift variant of China’s Long March 5 rocket family, primarily used to launch large modules for the Tiangong space station into low Earth orbit.
  • D. HuBERT
    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.
  • E. Transformer-XL
    Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
  • F. None of above.

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.