Triple

T824082
Position Surface form Disambiguated ID Type / Status
Subject OpenAI Baselines E17813 entity
Predicate implementsAlgorithm P8649 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: [OpenAI Baselines, implementsAlgorithm, DQN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DQN
Context triple: [OpenAI Baselines, implementsAlgorithm, 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. 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.
  • 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. 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.
  • E. DRL
    DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
  • 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_69a4937c9c188190aaa216f6b466f452 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2b503d48190bd4f33548a22d5fe completed March 1, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69a79291d51c81908163024842300a6e completed March 4, 2026, 2:01 a.m.
Created at: March 1, 2026, 7:38 p.m.