Triple

T11002363
Position Surface form Disambiguated ID Type / Status
Subject Helmholtz machine E260031 entity
Predicate inspired P9 FINISHED
Object variational autoencoder E40250 NE FINISHED

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: variational autoencoder | Statement: [Helmholtz machine, inspired, variational autoencoder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: variational autoencoder
Context triple: [Helmholtz machine, inspired, variational autoencoder]
  • A. variational autoencoders chosen
    Variational autoencoders are a class of generative neural networks that learn probabilistic latent representations of data, enabling them to generate new, similar samples.
  • B. Auto-Encoding Variational Bayes
    Auto-Encoding Variational Bayes is the foundational 2013 paper by Kingma and Welling that introduced variational autoencoders, a generative model framework combining deep learning with variational Bayesian inference.
  • C. VQ-VAE
    VQ-VAE is a neural network model that combines vector quantization with variational autoencoders to learn discrete latent representations for tasks like image and audio generation.
  • D. Helmholtz machine
    The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
  • E. Wasserstein GAN
    Wasserstein GAN is a variant of generative adversarial networks that improves training stability and sample quality by optimizing the Wasserstein (Earth Mover’s) distance between real and generated data distributions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d796d760008190930228fa77b61b8b completed April 9, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.