Triple

T8577087
Position Surface form Disambiguated ID Type / Status
Subject Ioannis Antonoglou E203073 entity
Predicate knownFor P22 FINISHED
Object DQN 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: DQN | Statement: [Ioannis Antonoglou, knownFor, DQN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DQN
Context triple: [Ioannis Antonoglou, knownFor, DQN]
  • A. 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.
  • 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. 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.
  • D. 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.
  • 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_69ca8328ebe481909a8c038fa79959b4 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbea97787481909ebbaa45f59cbdaa completed March 31, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce899dd7d48190b44338b92ad68bd0 completed April 2, 2026, 3:22 p.m.
Created at: March 30, 2026, 6:22 p.m.