Triple

T4277522
Position Surface form Disambiguated ID Type / Status
Subject TF-Agents E97077 entity
Predicate supportsAlgorithmFamily P203 FINISHED
Object DDPG E98481 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: DDPG | Statement: [TF-Agents, supportsAlgorithmFamily, DDPG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DDPG
Context triple: [TF-Agents, supportsAlgorithmFamily, DDPG]
  • A. DDPG chosen
    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. 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.
  • C. 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.
  • 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. Proximal Policy Optimization
    Proximal Policy Optimization is a popular reinforcement learning algorithm that improves policy gradient methods by using clipped objective functions to achieve stable and efficient training.
  • 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_69b34544be3c819084d1ab82d29f90c5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3501ef1388190b0c968b069014a59 completed March 12, 2026, 11:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b7b3b52c8190ae7c05448faf5558 completed March 14, 2026, 7:32 p.m.
Created at: March 12, 2026, 11:07 p.m.