Triple
T18723691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markov localization |
E457843
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Kalman filter |
—
|
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: Kalman filter | Statement: [Markov localization, relatedTo, Kalman filter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalman filter Context triple: [Markov localization, relatedTo, Kalman filter]
-
A.
Kalman filter
chosen
The Kalman filter is a mathematical algorithm used to estimate the changing state of a system from noisy measurements, widely applied in control systems, navigation, and signal processing.
-
B.
unscented Kalman filter
The unscented Kalman filter is a nonlinear state estimation algorithm that uses a deterministic sampling approach (sigma points) to more accurately capture the mean and covariance of a system than the standard extended Kalman filter.
-
C.
extended Kalman filter
The extended Kalman filter is a state estimation algorithm that generalizes the Kalman filter to nonlinear systems by linearizing about the current estimate, widely used in robotics and control for tracking and localization.
-
D.
Bayes filter
A Bayes filter is a probabilistic state-estimation method that recursively updates beliefs about a system’s state using Bayes’ theorem and incoming sensor data.
-
E.
“A New Approach to Linear Filtering and Prediction Problems”
“A New Approach to Linear Filtering and Prediction Problems” is Rudolf E. Kálmán’s landmark 1960 paper that introduced the Kalman filter, a foundational algorithm for optimal estimation in control theory, signal processing, and navigation.
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56abcfc048190a01dee959e768768 |
completed | April 19, 2026, 11:52 p.m. |
Created at: April 10, 2026, 11:50 a.m.