Triple
T1413886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Schulman |
E31866
|
entity |
| Predicate | notableWork |
P4
|
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: [John Schulman, notableWork, Trust Region Policy Optimization]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trust Region Policy Optimization Context triple: [John Schulman, notableWork, Trust Region Policy Optimization]
-
A.
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.
-
B.
Stable Baselines
Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
-
C.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
D.
RLlib
RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
-
E.
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.
- 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_69a49919a994819086528951bc224775 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3e476f08190aed1576805c62462 |
completed | March 1, 2026, 10:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ace57ec2d88190b06d0f20e3b52462 |
completed | March 8, 2026, 2:57 a.m. |
Created at: March 1, 2026, 7:59 p.m.