Triple

T23142529
Position Surface form Disambiguated ID Type / Status
Subject graph Laplacian E577498 entity
Predicate secondSmallestEigenvectorName P151078 FINISHED
Object Fiedler vector NE NERFINISHED

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: Fiedler vector | Statement: [graph Laplacian, secondSmallestEigenvectorName, Fiedler vector]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fiedler vector
Context triple: [graph Laplacian, secondSmallestEigenvectorName, Fiedler vector]
  • A. graph Laplacian
    The graph Laplacian is a matrix representation of a graph that encodes its connectivity and is fundamental in spectral graph theory, clustering, and network analysis.
  • B. Laplacian spectrum
    The Laplacian spectrum is the collection of eigenvalues of the Laplace operator on a domain or manifold, encoding how functions vibrate or diffuse over it and serving as a key tool in spectral geometry and mathematical physics.
  • C. Lyapunov vector
    A Lyapunov vector is a mathematical construct in dynamical systems theory that characterizes the directions in phase space associated with exponential growth or decay rates quantified by Lyapunov exponents.
  • D. Singular value decomposition
    Singular value decomposition is a fundamental matrix factorization technique that expresses a matrix as the product of two orthogonal (or unitary) matrices and a diagonal matrix of singular values, widely used in numerical analysis, data compression, and dimensionality reduction.
  • E. Courant–Fischer min–max theorem
    The Courant–Fischer min–max theorem is a fundamental result in linear algebra and spectral theory that characterizes the eigenvalues of a Hermitian (or symmetric) matrix via variational min–max principles over subspaces.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Fiedler vector
Target entity description: The Fiedler vector is the eigenvector associated with the second-smallest eigenvalue of a graph Laplacian, widely used to reveal community structure and perform spectral graph partitioning.
  • A. graph Laplacian
    The graph Laplacian is a matrix representation of a graph that encodes its connectivity and is fundamental in spectral graph theory, clustering, and network analysis.
  • B. Laplacian spectrum
    The Laplacian spectrum is the collection of eigenvalues of the Laplace operator on a domain or manifold, encoding how functions vibrate or diffuse over it and serving as a key tool in spectral geometry and mathematical physics.
  • C. Lyapunov vector
    A Lyapunov vector is a mathematical construct in dynamical systems theory that characterizes the directions in phase space associated with exponential growth or decay rates quantified by Lyapunov exponents.
  • D. Singular value decomposition
    Singular value decomposition is a fundamental matrix factorization technique that expresses a matrix as the product of two orthogonal (or unitary) matrices and a diagonal matrix of singular values, widely used in numerical analysis, data compression, and dimensionality reduction.
  • E. Courant–Fischer min–max theorem
    The Courant–Fischer min–max theorem is a fundamental result in linear algebra and spectral theory that characterizes the eigenvalues of a Hermitian (or symmetric) matrix via variational min–max principles over subspaces.
  • F. None of above. chosen
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: secondSmallestEigenvectorName
Context triple: [graph Laplacian, secondSmallestEigenvectorName, Fiedler vector]
  • A. secondElement
    Indicates that one entity is the second element in an ordered pair, sequence, or collection relative to another entity.
  • B. isSecondLargest
    Indicates that one entity has a value or size that is greater than all others except for a single larger entity, making it the second largest in the compared set.
  • C. secondSymbolValue
    Indicates that the value or quantity associated with the second symbol in a pair or sequence is being specified or referenced.
  • D. secondLetter
    Indicates that one entity is the second letter (in sequence or position) of another entity, typically a string or word.
  • E. secondLeader
    Indicates that an entity serves as the second-ranking leader or deputy leader in relation to another entity.
  • F. None of above. chosen

Provenance (4 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_69e245f8e6248190ba3d58e068b4dccb completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18ecb72fc8190a24e8f5756217a36 completed April 29, 2026, 4:53 a.m.
PD Predicate disambiguation batch_69ef89f83b108190aaaa1db6221fc163 completed April 27, 2026, 4:08 p.m.
PDg Predicate description generation batch_69ef9b7494f4819088ae59ea3d0ae8ab completed April 27, 2026, 5:23 p.m.
Created at: April 17, 2026, 4 p.m.