Triple
T18480217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toeplitz matrix |
E451536
|
entity |
| Predicate | hasAlgorithm |
P37563
|
FINISHED |
| Object |
Bareiss algorithm for Toeplitz systems
The Bareiss algorithm for Toeplitz systems is a specialized, numerically stable method for efficiently solving linear systems whose coefficient matrices have Toeplitz structure, exploiting that structure to reduce computational complexity.
|
E1326015
|
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: Bareiss algorithm for Toeplitz systems | Statement: [Toeplitz matrix, hasAlgorithm, Bareiss algorithm for Toeplitz systems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bareiss algorithm for Toeplitz systems Context triple: [Toeplitz matrix, hasAlgorithm, Bareiss algorithm for Toeplitz systems]
-
A.
Bartels–Stewart algorithm
The Bartels–Stewart algorithm is a numerical linear algebra method that efficiently solves certain matrix equations, particularly Sylvester and Lyapunov equations, using Schur decompositions.
-
B.
Toeplitz matrices
Toeplitz matrices are structured matrices whose entries are constant along each diagonal, playing a central role in operator theory, numerical analysis, and signal processing.
-
C.
Zassenhaus algorithm for factoring polynomials over the rationals
The Zassenhaus algorithm for factoring polynomials over the rationals is a classical computational method that reduces rational polynomial factorization to modular factorization and then recombines the results using lifting techniques.
-
D.
Berlekamp’s algorithm for factoring polynomials over finite fields
Berlekamp’s algorithm for factoring polynomials over finite fields is a foundational deterministic method in computational algebra that efficiently decomposes polynomials into irreducible factors over finite fields and underpins many modern algorithms in coding theory and cryptography.
-
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: Bareiss algorithm for Toeplitz systems Triple: [Toeplitz matrix, hasAlgorithm, Bareiss algorithm for Toeplitz systems]
Generated description
The Bareiss algorithm for Toeplitz systems is a specialized, numerically stable method for efficiently solving linear systems whose coefficient matrices have Toeplitz structure, exploiting that structure to reduce computational complexity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bareiss algorithm for Toeplitz systems Target entity description: The Bareiss algorithm for Toeplitz systems is a specialized, numerically stable method for efficiently solving linear systems whose coefficient matrices have Toeplitz structure, exploiting that structure to reduce computational complexity.
-
A.
Bartels–Stewart algorithm
The Bartels–Stewart algorithm is a numerical linear algebra method that efficiently solves certain matrix equations, particularly Sylvester and Lyapunov equations, using Schur decompositions.
-
B.
Toeplitz matrices
Toeplitz matrices are structured matrices whose entries are constant along each diagonal, playing a central role in operator theory, numerical analysis, and signal processing.
-
C.
Zassenhaus algorithm for factoring polynomials over the rationals
The Zassenhaus algorithm for factoring polynomials over the rationals is a classical computational method that reduces rational polynomial factorization to modular factorization and then recombines the results using lifting techniques.
-
D.
Berlekamp’s algorithm for factoring polynomials over finite fields
Berlekamp’s algorithm for factoring polynomials over finite fields is a foundational deterministic method in computational algebra that efficiently decomposes polynomials into irreducible factors over finite fields and underpins many modern algorithms in coding theory and cryptography.
-
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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53066a7108190a50eda9b489c90ca |
completed | April 19, 2026, 7:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a043f2f64848190808075254008e6e1 |
completed | May 13, 2026, 9:06 a.m. |
| NEDg | Description generation | batch_6a043fb7db388190bb50cfade4f025f9 |
completed | May 13, 2026, 9:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a044063c6048190b65620af8ceed897 |
completed | May 13, 2026, 9:12 a.m. |
Created at: April 10, 2026, 11:35 a.m.