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.