Triple

T4650847

Position	Surface form	Disambiguated ID	Type / Status
Subject	Probabilistic Robotics	`E102290`	entity
Predicate	topic	`P261`	FINISHED
Object	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.	`E457842`	NE FINISHED

How this triple was built (4 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: extended Kalman filter | Statement: [Probabilistic Robotics, topic, extended Kalman filter]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: extended Kalman filter
Context triple: [Probabilistic Robotics, topic, extended Kalman filter]

A. Kalman filter
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. “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.
C. Wiener filter
The Wiener filter is a signal processing technique that optimally estimates a desired signal from noisy observations by minimizing the mean square error, based on statistical properties of signal and noise.
D. Linear Estimation
Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
E. Kailath factorization in linear systems
Kailath factorization in linear systems is a matrix factorization technique used in control and signal processing to efficiently analyze and solve linear dynamical systems.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: extended Kalman filter
Triple: [Probabilistic Robotics, topic, extended Kalman filter]

Generated description

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.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: extended Kalman filter
Target entity description: 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.

A. Kalman filter
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. “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.
C. Wiener filter
The Wiener filter is a signal processing technique that optimally estimates a desired signal from noisy observations by minimizing the mean square error, based on statistical properties of signal and noise.
D. Linear Estimation
Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
E. Kailath factorization in linear systems
Kailath factorization in linear systems is a matrix factorization technique used in control and signal processing to efficiently analyze and solve linear dynamical systems.
F. None of above. chosen

Provenance (5 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_69bd43d71a308190afea7280841b0de8`	completed	March 20, 2026, 12:55 p.m.
NER	Named-entity recognition	`batch_69bd6302078081909451589d39c7b28c`	completed	March 20, 2026, 3:08 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69bdfae7636881908244b86cba1c66b7`	completed	March 21, 2026, 1:56 a.m.
NEDg	Description generation	`batch_69bdfbc12acc8190b8116a6003abb3e3`	completed	March 21, 2026, 2 a.m.
NED2	Entity disambiguation (via description)	`batch_69bdfc44536c8190a71e52b0690a7570`	completed	March 21, 2026, 2:02 a.m.

Created at: March 20, 2026, 1:14 p.m.