Triple
T18723712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monte Carlo localization |
E457844
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | probabilistic robotics method |
C40687
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: probabilistic robotics method Context triple: [Monte Carlo localization, instanceOf, probabilistic robotics method]
-
A.
behavior-based robotics paradigm
The behavior-based robotics paradigm is an approach to robot control that builds complex, adaptive behavior from the interaction and coordination of many simple, decentralized behavior modules directly coupled to sensors and actuators, rather than relying on centralized symbolic planning.
-
B.
robotics program
A robotics program is a structured course or initiative that teaches the design, construction, and programming of robots to solve real-world problems or complete specific tasks.
-
C.
probabilist
A probabilist is a mathematician or scientist who studies probability theory, focusing on the analysis and modeling of random phenomena and uncertainty.
-
D.
Monte Carlo reinforcement learning algorithm
A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
-
E.
autonomous navigation system
An autonomous navigation system is a self-directed control framework that enables vehicles or robots to perceive their environment, plan routes, and move safely to a destination without human intervention.
- F. None of above. chosen
Provenance (1 batch)
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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
Created at: April 10, 2026, 11:50 a.m.