Triple

T8737784
Position Surface form Disambiguated ID Type / Status
Subject Aaron van den Oord E207427 entity
Predicate notableWork P4 FINISHED
Object Representation Learning with Contrastive Predictive Coding E755722 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: Representation Learning with Contrastive Predictive Coding | Statement: [Aaron van den Oord, notableWork, Representation Learning with Contrastive Predictive Coding]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Representation Learning with Contrastive Predictive Coding
Context triple: [Aaron van den Oord, notableWork, Representation Learning with Contrastive Predictive Coding]
  • A. Contrastive Predictive Coding chosen
    Contrastive Predictive Coding is a self-supervised learning method that learns useful data representations by predicting future inputs in a latent space using a contrastive objective.
  • B. Neural Discrete Representation Learning
    Neural Discrete Representation Learning is a machine learning framework that introduces Vector Quantized Variational Autoencoders (VQ-VAE) to learn discrete latent representations for high-dimensional data such as images, audio, and video.
  • C. Generative Adversarial Networks
    Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
  • D. “A fast learning algorithm for deep belief nets”
    “A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
  • E. PixelCNN
    PixelCNN is a deep generative model that uses convolutional neural networks with autoregressive masking to model and generate images pixel by pixel.
  • 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_69ca835a03a081909d4d4cd01a18c9fb completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d45c96081909aa8509064ff3a04 completed March 31, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf517c6fac8190b782c8f441635814 completed April 3, 2026, 5:34 a.m.
Created at: March 30, 2026, 6:38 p.m.