Triple

T17585950
Position Surface form Disambiguated ID Type / Status
Subject Asynchronous Methods for Deep Reinforcement Learning E428322 entity
Predicate title P38 FINISHED
Object Asynchronous Methods for Deep Reinforcement Learning — 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: Asynchronous Methods for Deep Reinforcement Learning | Statement: [Asynchronous Methods for Deep Reinforcement Learning, title, Asynchronous Methods for Deep Reinforcement Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Asynchronous Methods for Deep Reinforcement Learning
Context triple: [Asynchronous Methods for Deep Reinforcement Learning, title, Asynchronous Methods for Deep Reinforcement Learning]
  • A. Asynchronous Methods for Deep Reinforcement Learning chosen
    "Asynchronous Methods for Deep Reinforcement Learning" is a 2016 DeepMind paper that introduced asynchronous parallel training techniques for deep reinforcement learning, most notably the A3C algorithm, enabling more stable and efficient learning without specialized hardware.
  • B. Asynchronous Advantage Actor-Critic
    Asynchronous Advantage Actor-Critic is a deep reinforcement learning algorithm that trains multiple parallel agents to learn both policy and value functions efficiently and stably.
  • C. Deep Q-Learning
    Deep Q-Learning is a reinforcement learning algorithm that uses deep neural networks to approximate Q-values, enabling agents to learn effective policies directly from high-dimensional inputs like raw images.
  • D. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
    "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures" is a research paper that introduces a highly scalable distributed reinforcement learning framework using an actor-learner architecture with importance weighting to enable efficient off-policy learning.
  • E. Proximal Policy Optimization
    Proximal Policy Optimization is a popular reinforcement learning algorithm that improves policy gradient methods by using clipped objective functions to achieve stable and efficient training.
  • 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.