Triple

T4470191
Position Surface form Disambiguated ID Type / Status
Subject Prioritized Experience Replay DQN E98475 entity
Predicate basedOn P98 FINISHED
Object Deep Q-Network 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: Deep Q-Network | Statement: [Prioritized Experience Replay DQN, basedOn, Deep Q-Network]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Deep Q-Network
Context triple: [Prioritized Experience Replay DQN, basedOn, Deep Q-Network]
  • 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. 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.
  • C. Dueling DQN
    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.
  • 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_69b3454b4ae481908967426dd37284d6 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b356b6a1f48190a39f5411648c40ff completed March 13, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6377154bc819099362e8b28698dbe completed March 15, 2026, 4:37 a.m.
Created at: March 12, 2026, 11:34 p.m.