Triple
T1311617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Kailath |
E28003
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Linear Least-Squares Estimation |
E29364
|
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: Linear Least-Squares Estimation | Statement: [Thomas Kailath, notableWork, Linear Least-Squares Estimation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linear Least-Squares Estimation Context triple: [Thomas Kailath, notableWork, Linear Least-Squares Estimation]
-
A.
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.
-
B.
method of least squares
chosen
The method of least squares is a fundamental mathematical technique for estimating unknown parameters by minimizing the sum of squared differences between observed and predicted values, widely used in statistics, data fitting, and regression analysis.
-
C.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
-
D.
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.
-
E.
Gauss–Markov theorem
The Gauss–Markov theorem is a fundamental result in statistics stating that, under certain conditions, the ordinary least squares estimator is the best linear unbiased estimator (BLUE) of the coefficients in a linear regression model.
- 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1560f888190bdd9107b08395e0b |
completed | March 1, 2026, 10:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbf2964048190950723487c7cf707 |
completed | March 8, 2026, 12:13 a.m. |
Created at: March 1, 2026, 7:55 p.m.