Triple
T3542937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Léon Bottou |
E74927
|
entity |
| Predicate | hasPublication |
P80
|
FINISHED |
| Object |
“Stochastic Gradient Descent Tricks”
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
|
E367291
|
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: “Stochastic Gradient Descent Tricks” | Statement: [Léon Bottou, hasPublication, “Stochastic Gradient Descent Tricks”]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “Stochastic Gradient Descent Tricks” Context triple: [Léon Bottou, hasPublication, “Stochastic Gradient Descent Tricks”]
-
A.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
B.
“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.
-
C.
Large-Scale Distributed Deep Networks
Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
-
D.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
E.
“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.
- 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: “Stochastic Gradient Descent Tricks” Triple: [Léon Bottou, hasPublication, “Stochastic Gradient Descent Tricks”]
Generated description
“Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: “Stochastic Gradient Descent Tricks” Target entity description: “Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
-
A.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
B.
“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.
-
C.
Large-Scale Distributed Deep Networks
Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
-
D.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
E.
“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.
- 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_69ad85d274cc8190ab59c97298a1cfbf |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbf752dd481909226044ffe595338 |
completed | March 8, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bdd0cb4819086119b54c2708850 |
completed | March 13, 2026, 4 a.m. |
| NEDg | Description generation | batch_69b38cb6a2188190b68f4903144a0e51 |
completed | March 13, 2026, 4:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b39062a10c8190bc227c02cf4f3ab1 |
completed | March 13, 2026, 4:19 a.m. |
Created at: March 8, 2026, 3:20 p.m.