Triple

T1793196
Position Surface form Disambiguated ID Type / Status
Subject Atari deep Q-network E39543 entity
Predicate inspiredAlgorithm P7215 FINISHED
Object Double DQN E101969 NE FINISHED

How this triple was built (3 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: Double DQN | Statement: [Atari deep Q-network, inspiredAlgorithm, Double DQN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Double DQN
Context triple: [Atari deep Q-network, inspiredAlgorithm, Double DQN]
  • A. Double DQN chosen
    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. 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.
  • C. 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.
  • 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. 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.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: inspiredAlgorithm
Context triple: [Atari deep Q-network, inspiredAlgorithm, Double DQN]
  • A. relatedAlgorithm
    Indicates that one algorithm has a meaningful connection or association with another algorithm, such as similarity, dependency, or complementary function.
  • B. inspiredDevelopmentOf chosen
    Indicates that one entity served as a motivating influence or creative stimulus leading to the development or creation of another entity.
  • C. inspiredField
    Indicates that one entity served as a source of inspiration or influence for the development, direction, or characteristics of a particular field or domain.
  • D. inspiredArtist
    Indicates that one artist has served as a source of creative influence or inspiration for another artist.
  • E. algorithmType
    Indicates the specific kind or category of algorithm associated with an entity or process.
  • F. None of above.

Provenance (4 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_69ab61b6ea188190aab9fb839bf1e367 completed March 6, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69adb5d26afc81909675064289d3a5b8 completed March 8, 2026, 5:45 p.m.
PD Predicate disambiguation batch_69aa61d2f7a8819090301f92d3e358c7 completed March 6, 2026, 5:10 a.m.
Created at: March 4, 2026, 7:32 p.m.