Triple
T17738637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timothy P. Lillicrap |
E442790
|
entity |
| Predicate | coAuthorOf |
P2389
|
FINISHED |
| Object | Continuous control with deep reinforcement learning |
—
|
NE NERFINISHED |
Disambiguation candidates (2 decisions)
The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Continuous control with deep reinforcement learning Context triple: [Timothy P. Lillicrap, coAuthorOf, Continuous control with deep reinforcement learning]
-
A.
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.
-
B.
Natural Policy Gradient
Natural Policy Gradient is a reinforcement learning optimization method that improves policy gradient updates by accounting for the geometry of the parameter space using the Fisher information matrix, leading to more stable and efficient learning.
-
C.
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.
-
D.
Soft Actor-Critic
Soft Actor-Critic is a model-free deep reinforcement learning algorithm that combines off-policy learning with entropy maximization to achieve stable and sample-efficient continuous control.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Continuous control with deep reinforcement learning Target entity description: "Continuous control with deep reinforcement learning" is a highly influential research paper that introduced deep neural network methods for solving continuous-action reinforcement learning tasks, notably using deterministic policy gradients.
-
A.
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.
-
B.
Natural Policy Gradient
Natural Policy Gradient is a reinforcement learning optimization method that improves policy gradient updates by accounting for the geometry of the parameter space using the Fisher information matrix, leading to more stable and efficient learning.
-
C.
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.
-
D.
Soft Actor-Critic
Soft Actor-Critic is a model-free deep reinforcement learning algorithm that combines off-policy learning with entropy maximization to achieve stable and sample-efficient continuous control.
-
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. chosen
Provenance (2 batches)
| Stage | Batch ID | Job type | Status |
|---|---|---|---|
| creating | batch_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
elicitation | completed |
| NER | batch_69e47acb05848190a4b7edb98f15b8c6 |
ner | completed |
Created at: April 10, 2026, 10:09 a.m.