Triple

T8482807
Position Surface form Disambiguated ID Type / Status
Subject Volodymyr Mnih E200558 entity
Predicate coAuthorOf P2389 FINISHED
Object Human-level control through deep reinforcement learning E39543 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: Human-level control through deep reinforcement learning | Statement: [Volodymyr Mnih, coAuthorOf, Human-level control through deep reinforcement learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Human-level control through deep reinforcement learning
Context triple: [Volodymyr Mnih, coAuthorOf, Human-level control through deep reinforcement learning]
  • A. Atari deep Q-network chosen
    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.
  • 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. Natural Policy Gradient
    Natural Policy Gradient is a reinforcement learning optimization method that improves policy gradient updates by accounting for the geometry of the parameter space using the Fisher information matrix, leading to more stable and efficient 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. 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 (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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe53845e881909eeb32863c7aa942 completed March 31, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4de95e3081908277c65598f3884a completed April 2, 2026, 11:07 a.m.
Created at: March 30, 2026, 6:12 p.m.