Triple
T8216769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalman filter |
E191951
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Wiener filter |
E158221
|
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: Wiener filter | Statement: [Kalman filter, relatedTo, Wiener filter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wiener filter Context triple: [Kalman filter, relatedTo, Wiener filter]
-
A.
Wiener filter
chosen
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.
-
B.
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.
-
C.
“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.
-
D.
Wiener–Khinchin theorem
The Wiener–Khinchin theorem is a fundamental result in signal processing and probability theory that relates a wide-sense stationary random process’s autocorrelation function to its power spectral density via the Fourier transform.
-
E.
Fourier
Fourier is a French surname most famously associated with Jean-Baptiste Joseph Fourier, the mathematician and physicist known for developing Fourier analysis and Fourier series.
- 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_69ca82c8c054819087fedd9a5436b8a3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb776f41108190bed1c6a8ddbea374 |
completed | March 31, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccedfb6f608190aebfa720b56325e5 |
completed | April 1, 2026, 10:05 a.m. |
Created at: March 30, 2026, 5:44 p.m.