Triple

T1793197
Position Surface form Disambiguated ID Type / Status
Subject Atari deep Q-network E39543 entity
Predicate inspiredAlgorithm P7215 FINISHED
Object Dueling DQN E98474 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: Dueling DQN | Statement: [Atari deep Q-network, inspiredAlgorithm, Dueling DQN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dueling DQN
Context triple: [Atari deep Q-network, inspiredAlgorithm, Dueling DQN]
  • 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. 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.
  • C. 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.
  • D. Prioritized Experience Replay DQN
    Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
  • E. 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.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abaffee0f88190aa7a42ef4a4e2bd2 completed March 7, 2026, 4:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69adb5d26afc81909675064289d3a5b8 completed March 8, 2026, 5:45 p.m.
Created at: March 4, 2026, 7:32 p.m.