Triple
T4293716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stable Baselines |
E99657
|
entity |
| Predicate | supportsAlgorithm |
P203
|
FINISHED |
| Object | PPO |
E98478
|
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: PPO | Statement: [Stable Baselines, supportsAlgorithm, PPO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PPO Context triple: [Stable Baselines, supportsAlgorithm, PPO]
-
A.
PPO
chosen
PPO (Proximal Policy Optimization) is a popular reinforcement learning algorithm known for its stability and sample efficiency in training complex policies, especially in continuous control and high-dimensional environments.
-
B.
PPO2
PPO2 is an improved variant of the Proximal Policy Optimization reinforcement learning algorithm, designed for stable and efficient policy gradient training in continuous and discrete control tasks.
-
C.
TRPO
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.
-
D.
PO
PO is a UK postcode area covering Portsmouth and surrounding parts of Hampshire and West Sussex.
-
E.
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.
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35082228081908504e3fd7c4ca1e8 |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c73d47448190a844bc13eae84a54 |
completed | March 14, 2026, 8:38 p.m. |
Created at: March 12, 2026, 11:08 p.m.