Triple
T7027396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Generalized Advantage Estimation |
E163182
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | Trust Region Policy Optimization |
E98480
|
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: Trust Region Policy Optimization | Statement: [Generalized Advantage Estimation, compatibleWith, Trust Region Policy Optimization]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trust Region Policy Optimization Context triple: [Generalized Advantage Estimation, compatibleWith, Trust Region Policy Optimization]
-
A.
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.
-
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.
Actor-Critic using Kronecker-Factored Trust Region
Actor-Critic using Kronecker-Factored Trust Region (ACKTR) is a reinforcement learning algorithm that improves sample efficiency and stability by applying Kronecker-factored approximate curvature to natural gradient updates in actor-critic methods.
-
D.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
E.
TRPO
chosen
TRPO (Trust Region Policy Optimization) is a reinforcement learning algorithm that optimizes policies with guaranteed monotonic improvement by constraining each update within a trust region to maintain stability.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fee32081908eff988b18daa6d0 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c78857d23c8190904a90459a802cb8 |
completed | March 28, 2026, 7:50 a.m. |
Created at: March 27, 2026, 2:35 p.m.