Triple

T8482797
Position Surface form Disambiguated ID Type / Status
Subject Volodymyr Mnih E200558 entity
Predicate knownFor P22 FINISHED
Object DQN algorithm E444494 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: DQN algorithm | Statement: [Volodymyr Mnih, knownFor, DQN algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DQN algorithm
Context triple: [Volodymyr Mnih, knownFor, DQN algorithm]
  • A. Double DQN
    Double DQN is a reinforcement learning algorithm that improves upon standard Deep Q-Networks by reducing overestimation bias through decoupling action selection from action evaluation.
  • B. Q-learning
    Q-learning is a model-free reinforcement learning algorithm that learns an action-value function to optimize decision-making by estimating the expected cumulative reward for each state-action pair.
  • C. Deep Q-Learning chosen
    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. DDPG
    DDPG (Deep Deterministic Policy Gradient) is a model-free, off-policy deep reinforcement learning algorithm designed for continuous action spaces, combining ideas from DQN and actor-critic methods.
  • E. Rainbow DQN
    Rainbow DQN is a deep reinforcement learning algorithm that combines several key extensions to the original DQN—such as double Q-learning, prioritized replay, dueling networks, multi-step learning, distributional RL, and noisy nets—into a single, more performant agent.
  • 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_69ce3a2b2e9081909f19712946c6ec20 completed April 2, 2026, 9:43 a.m.
Created at: March 30, 2026, 6:12 p.m.