Triple

T10991207
Position Surface form Disambiguated ID Type / Status
Subject random matrix theory E259756 entity
Predicate hasKeyConcept P533 FINISHED
Object Ginibre ensemble
The Ginibre ensemble is a fundamental class of non-Hermitian random matrices with independently distributed complex (or real/quaternion) Gaussian entries, widely studied for its rich eigenvalue statistics in random matrix theory.
E898467 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: Ginibre ensemble | Statement: [random matrix theory, hasKeyConcept, Ginibre ensemble]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ginibre ensemble
Context triple: [random matrix theory, hasKeyConcept, Ginibre ensemble]
  • A. Gaussian orthogonal ensemble
    The Gaussian orthogonal ensemble is a fundamental random matrix ensemble of real symmetric matrices with Gaussian-distributed entries, central to the study of eigenvalue statistics and universality in random matrix theory.
  • B. Gaussian unitary ensemble
    The Gaussian unitary ensemble is a fundamental random matrix ensemble of complex Hermitian matrices with statistically independent, Gaussian-distributed entries, central to quantum chaos and random matrix theory.
  • C. Jacobi ensemble
    The Jacobi ensemble is a family of random matrix models whose eigenvalue distributions are supported on a finite interval and are closely connected to classical orthogonal polynomials and beta-type probability measures.
  • D. Gaussian symplectic ensemble
    The Gaussian symplectic ensemble is a random matrix ensemble of self-dual quaternionic Hermitian matrices used in random matrix theory to model systems with time-reversal symmetry and strong spin–orbit coupling.
  • E. Wigner surmise
    The Wigner surmise is an approximate formula in random matrix theory that describes the statistical distribution of spacings between neighboring energy levels in complex quantum 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: Ginibre ensemble
Triple: [random matrix theory, hasKeyConcept, Ginibre ensemble]
Generated description
The Ginibre ensemble is a fundamental class of non-Hermitian random matrices with independently distributed complex (or real/quaternion) Gaussian entries, widely studied for its rich eigenvalue statistics in random matrix theory.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ginibre ensemble
Target entity description: The Ginibre ensemble is a fundamental class of non-Hermitian random matrices with independently distributed complex (or real/quaternion) Gaussian entries, widely studied for its rich eigenvalue statistics in random matrix theory.
  • A. Gaussian orthogonal ensemble
    The Gaussian orthogonal ensemble is a fundamental random matrix ensemble of real symmetric matrices with Gaussian-distributed entries, central to the study of eigenvalue statistics and universality in random matrix theory.
  • B. Gaussian unitary ensemble
    The Gaussian unitary ensemble is a fundamental random matrix ensemble of complex Hermitian matrices with statistically independent, Gaussian-distributed entries, central to quantum chaos and random matrix theory.
  • C. Jacobi ensemble
    The Jacobi ensemble is a family of random matrix models whose eigenvalue distributions are supported on a finite interval and are closely connected to classical orthogonal polynomials and beta-type probability measures.
  • D. Gaussian symplectic ensemble
    The Gaussian symplectic ensemble is a random matrix ensemble of self-dual quaternionic Hermitian matrices used in random matrix theory to model systems with time-reversal symmetry and strong spin–orbit coupling.
  • E. Wigner surmise
    The Wigner surmise is an approximate formula in random matrix theory that describes the statistical distribution of spacings between neighboring energy levels in complex quantum 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d787b8d7088190b7d6b63c3bac4ad1 completed April 9, 2026, 11:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69e34504ebec8190a78e4795765b0c24 completed April 18, 2026, 8:47 a.m.
NEDg Description generation batch_69e3556fd3548190a33f04604be947cf completed April 18, 2026, 9:57 a.m.
NED2 Entity disambiguation (via description) batch_69e3593b0f8481909ed7a90f8bb9839d completed April 18, 2026, 10:13 a.m.
Created at: April 8, 2026, 9:24 p.m.