Triple

T22819688
Position Surface form Disambiguated ID Type / Status
Subject AdaGrad E565192 entity
Predicate fullName P16 FINISHED
Object Adaptive Gradient Algorithm — NE NERFINISHED

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: Adaptive Gradient Algorithm | Statement: [AdaGrad, fullName, Adaptive Gradient Algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Adaptive Gradient Algorithm
Context triple: [AdaGrad, fullName, Adaptive Gradient Algorithm]
  • A. “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.
  • B. Gauss–Newton optimization
    Gauss–Newton optimization is an iterative numerical method for solving non-linear least squares problems by repeatedly linearizing the model around the current estimate and updating parameters to minimize the squared error.
  • C. 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.
  • D. AdaGrad chosen
    AdaGrad is an adaptive gradient descent optimization algorithm that adjusts learning rates for individual parameters based on their historical gradients, often improving convergence in sparse settings.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458426188190b58b8ab4844fe420 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17dcf39a88190bec26affc304236d completed April 29, 2026, 3:41 a.m.
Created at: April 17, 2026, 3:33 p.m.