Triple

T17585908
Position Surface form Disambiguated ID Type / Status
Subject Asynchronous Advantage Actor-Critic E428319 entity
Predicate optimizationMethod P859 FINISHED
Object RMSProp — 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: RMSProp | Statement: [Asynchronous Advantage Actor-Critic, optimizationMethod, RMSProp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RMSProp
Context triple: [Asynchronous Advantage Actor-Critic, optimizationMethod, RMSProp]
  • A. RMSProp chosen
    RMSProp is an adaptive gradient-based optimization algorithm commonly used to efficiently train deep neural networks by adjusting learning rates for individual parameters.
  • B. AdaGrad
    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.
  • C. Adam optimizer
    The Adam optimizer is a popular stochastic gradient descent method in machine learning that adaptively adjusts learning rates for each parameter using estimates of first and second moments of gradients.
  • D. AdaDelta
    AdaDelta is an adaptive learning rate optimization algorithm for training neural networks that improves upon methods like RMSProp by eliminating the need to manually set a global learning rate.
  • E. “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.
  • 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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e463d22f908190ae0f1eeafbe54459 completed April 19, 2026, 5:10 a.m.
Created at: April 10, 2026, 5:50 a.m.