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.