Triple
T4470368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PPO |
E98478
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object | RLlib |
E95190
|
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: RLlib | Statement: [PPO, implementedIn, RLlib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RLlib Context triple: [PPO, implementedIn, RLlib]
-
A.
RLlib
chosen
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.
-
B.
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.
-
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.
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.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356b6a1f48190a39f5411648c40ff |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6286c75b08190bd683d300f6c97f0 |
completed | March 15, 2026, 3:33 a.m. |
Created at: March 12, 2026, 11:34 p.m.