Triple

T93176
Position Surface form Disambiguated ID Type / Status
Subject Geoffrey Hinton E1872 entity
Predicate notableWork P4 FINISHED
Object “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.
E11232 NE FINISHED

How this triple was built (4 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: [Geoffrey Hinton, notableWork, “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: [Geoffrey Hinton, notableWork, “A fast learning algorithm for deep belief nets”]
  • A. Boltzmann machines
    Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
  • B. “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.
  • C. Deep Learning (book)
    Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
  • D. Lifelong Learning Machines program
    The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
  • E. Google Brain
    Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: “A fast learning algorithm for deep belief nets”
Triple: [Geoffrey Hinton, notableWork, “A fast learning algorithm for deep belief nets”]
Generated description
“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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: “A fast learning algorithm for deep belief nets”
Target entity description: “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.
  • A. Boltzmann machines
    Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
  • B. “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.
  • C. Deep Learning (book)
    Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
  • D. Lifelong Learning Machines program
    The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
  • E. Google Brain
    Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
  • 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_69a24d1a97dc819094e6c021fe9b05a7 completed Feb. 28, 2026, 2:04 a.m.
NER Named-entity recognition batch_69a24fd28e988190bde699647ee5b16b completed Feb. 28, 2026, 2:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a27c0147d481909c62cd45c8079519 completed Feb. 28, 2026, 5:24 a.m.
NEDg Description generation batch_69a27c81e0d481909029bbe7b9c04ab0 completed Feb. 28, 2026, 5:26 a.m.
NED2 Entity disambiguation (via description) batch_69a27cf9cc6c8190b8e666ea21c331f2 completed Feb. 28, 2026, 5:28 a.m.
Created at: Feb. 28, 2026, 2:07 a.m.