Triple
T17521266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MuJoCo environments |
E426682
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object | Humanoid-v2 |
—
|
NE NERFINISHED |
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: Humanoid-v2 | Statement: [MuJoCo environments, includes, Humanoid-v2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Humanoid-v2 Context triple: [MuJoCo environments, includes, Humanoid-v2]
-
A.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
B.
HUBO humanoid robot series
The HUBO humanoid robot series is a line of advanced bipedal robots from South Korea known for their human-like mobility, participation in robotics competitions, and contributions to humanoid robotics research.
-
C.
DRC-HUBO
DRC-HUBO is a Korean-developed humanoid robot renowned for winning the DARPA Robotics Challenge by demonstrating advanced mobility and disaster-response capabilities.
-
D.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
E.
OpenAI Gym
chosen
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.