Triple

T11978119
Position Surface form Disambiguated ID Type / Status
Subject Introduction to Stochastic Control Theory E285086 entity
Predicate subject P450 FINISHED
Object Kalman filtering E191951 NE FINISHED

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 filtering | Statement: [Introduction to Stochastic Control Theory, subject, Kalman filtering]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kalman filtering
Context triple: [Introduction to Stochastic Control Theory, subject, Kalman filtering]
  • 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. “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. 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.
  • D. 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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab2eaeb881909f7914758f859413 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90393cfb08190b5b45d3e5e32fad3 completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f471f6afc48190856a0f7c486b28aa completed May 1, 2026, 9:27 a.m.
Created at: April 8, 2026, 9:46 p.m.