Triple
T6993316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Proximal Policy Optimization |
E162136
|
entity |
| Predicate | evaluationBenchmarks |
P23745
|
FINISHED |
| Object | OpenAI Gym |
E17413
|
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: OpenAI Gym | Statement: [Proximal Policy Optimization, evaluationBenchmarks, OpenAI Gym]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OpenAI Gym Context triple: [Proximal Policy Optimization, evaluationBenchmarks, OpenAI Gym]
-
A.
OpenAI Gym
chosen
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
D.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
E.
TF-Agents
TF-Agents is an open-source library built on TensorFlow that provides modular components and tools for developing, training, and evaluating reinforcement learning algorithms.
- 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: evaluationBenchmarks Context triple: [Proximal Policy Optimization, evaluationBenchmarks, OpenAI Gym]
-
A.
evaluationBasis
Indicates the criteria, standards, or reference framework used to judge, assess, or measure something in an evaluation process.
-
B.
benchmarkFor
chosen
Indicates that one entity serves as a standard or reference point against which the performance, quality, or characteristics of another entity are measured or evaluated.
-
C.
primaryBenchmarkProvider
Indicates that one entity serves as the main or default source of benchmark data or performance standards for another entity.
-
D.
benchmarkStatus
Indicates the current evaluation state or outcome of a benchmark process applied to an entity or system.
-
E.
evaluationFunction
Indicates a relationship where a specific procedure or rule assigns a value or score to an input, typically to assess its quality, utility, or desirability.
- 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_69c68856d7808190ab33ee914640281b |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbc30fdc81909244d83c8178755c |
completed | March 27, 2026, 7:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a161f088190bbc3c4e2815fa929 |
completed | March 28, 2026, 5:41 a.m. |
| PD | Predicate disambiguation | batch_69c6d7c4a18881908d267137daed828b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:32 p.m.