Triple
T1793184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atari deep Q-network |
E39543
|
entity |
| Predicate | environmentFramework |
P9140
|
FINISHED |
| Object | Arcade Learning Environment |
E95193
|
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: Arcade Learning Environment | Statement: [Atari deep Q-network, environmentFramework, Arcade Learning Environment]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arcade Learning Environment Context triple: [Atari deep Q-network, environmentFramework, Arcade Learning Environment]
-
A.
Arcade Learning Environment
chosen
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
B.
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.
-
C.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
D.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
E.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
- 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: environmentFramework Context triple: [Atari deep Q-network, environmentFramework, Arcade Learning Environment]
-
A.
environmentType
Indicates the kind or category of environment associated with an entity or situation.
-
B.
supportsEnvironment
Indicates that one entity provides the necessary conditions, compatibility, or resources for another entity to operate or exist within a particular environment.
-
C.
frameworkFor
chosen
Indicates that one entity serves as a supporting structure, system, or basis that organizes, guides, or enables the development or functioning of another entity.
-
D.
environmentalMedium
Indicates the environmental context or medium (such as air, water, or soil) through which a substance, effect, or process occurs or is present.
-
E.
primaryOperatingEnvironment
Indicates the main environment, platform, or context in which an entity is primarily designed to operate or function.
- 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.