Triple

T16249643
Position Surface form Disambiguated ID Type / Status
Subject Laplacian spectrum E394466 entity
Predicate usedIn P98 FINISHED
Object spectral graph theory E621125 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: spectral graph theory | Statement: [Laplacian spectrum, usedIn, spectral graph theory]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: spectral graph theory
Context triple: [Laplacian spectrum, usedIn, spectral graph theory]
  • A. Spectral Graph Theory chosen
    Spectral Graph Theory is a mathematical field that studies graphs through the eigenvalues and eigenvectors of matrices associated with them, such as adjacency and Laplacian matrices, with applications across combinatorics, computer science, 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. graph theory
    Graph theory is a branch of mathematics that studies the properties and applications of graphs, which model pairwise relationships between objects using vertices and edges.
  • D. Convex Optimization of Graph Laplacian Eigenvalues
    "Convex Optimization of Graph Laplacian Eigenvalues" is a research work by Stephen P. Boyd that develops convex optimization methods to analyze and design graphs via the spectral properties of their Laplacian matrices.
  • E. Alon–Boppana bound
    The Alon–Boppana bound is a fundamental result in spectral graph theory that gives an asymptotic lower bound on the second-largest eigenvalue of large regular graphs, showing inherent limitations on how well such graphs can approximate expanders.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24594f23c8190bd59fcb2585cb5e3 completed April 17, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000ee568a48190835ce76f84461044 completed May 10, 2026, 4:51 a.m.
Created at: April 10, 2026, 5:04 a.m.