Triple
T18723614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | extended Kalman filter |
E457842
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | nonlinear state estimation 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: nonlinear state estimation algorithm Context triple: [extended Kalman filter, instanceOf, nonlinear state estimation 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.
redescending M-estimator
A redescending M-estimator is a robust statistical estimator whose influence function decreases back toward zero for large residuals, thereby downweighting extreme outliers more strongly than standard M-estimators.
-
C.
control theory conference
A control theory conference is a professional gathering where researchers, practitioners, and students present, discuss, and advance methods for analyzing and designing systems that regulate dynamic behavior.
-
D.
composite estimator
A composite estimator is a statistical estimator formed by combining two or more individual estimators, often through weighted averaging, to improve overall accuracy, stability, or robustness of parameter estimates.
-
E.
model-based reinforcement learning algorithm
A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
- 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.