Triple

T2169496
Position Surface form Disambiguated ID Type / Status
Subject Deep belief networks E46988 entity
Predicate describedIn P519 FINISHED
Object "A Fast Learning Algorithm for Deep Belief Nets" E11232 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: "A Fast Learning Algorithm for Deep Belief Nets" | Statement: [Deep belief networks, describedIn, "A Fast Learning Algorithm for Deep Belief Nets"]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: "A Fast Learning Algorithm for Deep Belief Nets"
Context triple: [Deep belief networks, describedIn, "A Fast Learning Algorithm for Deep Belief Nets"]
  • A. “A fast learning algorithm for deep belief nets” chosen
    “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.
  • B. Deep belief networks
    Deep belief networks are a class of deep generative neural network models composed of stacked layers of latent variables, typically built from restricted Boltzmann machines, used for unsupervised feature learning and representation.
  • C. Boltzmann machines
    Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
  • D. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • E. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • 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_69a88a184cbc8190877791f6552c2484 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbeaeb58881908ad34f7b253bac2a completed March 7, 2026, 5:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58f511a08190880fbde8900d59df completed March 9, 2026, 5:21 a.m.
Created at: March 4, 2026, 7:45 p.m.