Triple

T8958303
Position Surface form Disambiguated ID Type / Status
Subject Sergey Ioffe E213533 entity
Predicate coAuthorOf P2389 FINISHED
Object Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift E701500 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: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift | Statement: [Sergey Ioffe, coAuthorOf, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Context triple: [Sergey Ioffe, coAuthorOf, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift]
  • A. Batch Normalization chosen
    Batch Normalization is a deep learning technique that stabilizes and accelerates neural network training by normalizing layer inputs using mini-batch statistics.
  • B. “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.
  • C. 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.
  • D. 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.
  • E. “Large-Scale Machine Learning with Stochastic Gradient Descent”
    “Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
  • 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_69ca8399ad2081909f8fa41d4314c215 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6729ab7c8190a6168f0aa70a5520 completed April 1, 2026, 12:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd09e378c8190bb9f5d78a3b91fe7 completed April 3, 2026, 2:37 p.m.
Created at: March 30, 2026, 7 p.m.