Triple

T20811626
Position Surface form Disambiguated ID Type / Status
Subject Tom Schaul E512320 entity
Predicate coAuthorOf P2389 FINISHED
Object Dueling Network Architectures 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: Dueling Network Architectures for Deep Reinforcement Learning | Statement: [Tom Schaul, coAuthorOf, Dueling Network Architectures for Deep Reinforcement Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dueling Network Architectures for Deep Reinforcement Learning
Context triple: [Tom Schaul, coAuthorOf, Dueling Network Architectures for Deep Reinforcement Learning]
  • A. Dueling DQN chosen
    Dueling DQN is a deep reinforcement learning algorithm that separates state-value and advantage estimations within its neural network architecture to improve learning efficiency and stability over standard DQN.
  • B. 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.
  • C. 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.
  • D. Asynchronous Methods for Deep Reinforcement Learning
    "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.
  • E. Atari deep Q-network
    The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
  • 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_69e0b4cd25088190b48ca9700cd24efc completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2d338ac819096d4a33de831609e completed April 21, 2026, 12:20 a.m.
Created at: April 16, 2026, 12:40 p.m.