Triple
T18723713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monte Carlo localization |
E457844
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | particle filter algorithm |
C6819
|
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: particle filter algorithm Context triple: [Monte Carlo localization, instanceOf, particle filter algorithm]
-
A.
algorithm
chosen
An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
-
B.
particle detector
A particle detector is a device or system that identifies, tracks, and measures properties of subatomic particles produced in physical processes or experiments.
-
C.
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.
-
D.
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.
-
E.
partition-based clustering method
A partition-based clustering method is an approach that divides a dataset into a predefined number of non-overlapping groups (clusters) by directly assigning each data point to exactly one cluster based on a chosen similarity or distance measure.
- F. None of above.
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.