Triple

T824081
Position Surface form Disambiguated ID Type / Status
Subject OpenAI Baselines E17813 entity
Predicate implementsAlgorithm P8649 FINISHED
Object Deep Q-Network E39543 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: Deep Q-Network | Statement: [OpenAI Baselines, implementsAlgorithm, Deep Q-Network]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Deep Q-Network
Context triple: [OpenAI Baselines, implementsAlgorithm, 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. MuZero
    MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
  • C. Arcade Learning Environment
    Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
  • D. OpenAI Baselines
    OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
  • 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.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: implementsAlgorithm
Context triple: [OpenAI Baselines, implementsAlgorithm, Deep Q-Network]
  • A. usesEncryptionAlgorithm
    Indicates that one entity applies or relies on a specific encryption algorithm to protect data or communications.
  • B. usesImplement
    Indicates that one entity employs or makes use of another entity as a tool, instrument, or means to perform an action or achieve a purpose.
  • C. canBeImplementedWith chosen
    Indicates that one entity is capable of being realized, executed, or fulfilled through the use or application of another entity.
  • D. implementedUnder
    Indicates that an action, policy, or process is carried out within the scope, authority, or framework defined by a particular higher-level plan, rule, or governing entity.
  • E. checkDigitAlgorithm
    Indicates the algorithm or method used to compute or validate a check digit for an identifier or code.
  • 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_69a4937c9c188190aaa216f6b466f452 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ab7d3984819089aefbf12d3b3c2c completed March 1, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d93af548190818c14a370e0914a completed March 3, 2026, 11:24 p.m.
PD Predicate disambiguation batch_69a4aa781e1081909df006f730296c53 completed March 1, 2026, 9:07 p.m.
Created at: March 1, 2026, 7:38 p.m.