Triple

T20627234
Position Surface form Disambiguated ID Type / Status
Subject Minkowski’s theorem on convex sets E506850 entity
Predicate relatedTo P37 FINISHED
Object Minkowski’s second theorem NE NERFINISHED

How this triple was built (3 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: Minkowski’s second theorem | Statement: [Minkowski’s theorem on convex sets, relatedTo, Minkowski’s second theorem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Minkowski’s second theorem
Context triple: [Minkowski’s theorem on convex sets, relatedTo, Minkowski’s second theorem]
  • A. Minkowski’s theorem on convex sets
    Minkowski’s theorem on convex sets is a fundamental result in convex geometry that characterizes lattice points in convex bodies, underpinning much of the theory of convex polytopes and the geometry of numbers.
  • B. Hermite–Minkowski theorem
    The Hermite–Minkowski theorem is a fundamental result in algebraic number theory that gives a finiteness bound on the number of number fields of a given degree and discriminant.
  • C. Mazur’s theorem on convex sets
    Mazur’s theorem on convex sets is a fundamental result in functional analysis that characterizes the structure and approximation properties of convex sets in Banach spaces, particularly via convex combinations of sequences.
  • D. Siegel’s lemma
    Siegel’s lemma is a result in number theory that guarantees the existence of small-height integer solutions to systems of linear equations with integer coefficients.
  • E. Carathéodory’s theorem in convex geometry
    Carathéodory’s theorem in convex geometry is a fundamental result stating that any point in the convex hull of a set in ℝⁿ can be expressed as a convex combination of at most n+1 points from that set.
  • 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: Minkowski’s second theorem
Target entity description: Minkowski’s second theorem is a fundamental result in the geometry of numbers that relates the successive minima of a convex, symmetric body in Euclidean space to its volume and the determinant of a lattice.
  • A. Minkowski’s theorem on convex sets
    Minkowski’s theorem on convex sets is a fundamental result in convex geometry that characterizes lattice points in convex bodies, underpinning much of the theory of convex polytopes and the geometry of numbers.
  • B. Hermite–Minkowski theorem
    The Hermite–Minkowski theorem is a fundamental result in algebraic number theory that gives a finiteness bound on the number of number fields of a given degree and discriminant.
  • C. Mazur’s theorem on convex sets
    Mazur’s theorem on convex sets is a fundamental result in functional analysis that characterizes the structure and approximation properties of convex sets in Banach spaces, particularly via convex combinations of sequences.
  • D. Siegel’s lemma
    Siegel’s lemma is a result in number theory that guarantees the existence of small-height integer solutions to systems of linear equations with integer coefficients.
  • E. Carathéodory’s theorem in convex geometry
    Carathéodory’s theorem in convex geometry is a fundamental result stating that any point in the convex hull of a set in ℝⁿ can be expressed as a convex combination of at most n+1 points from that set.
  • F. None of above. chosen

Provenance (2 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_69e0b4bd4a0081908d4e97a590a33fb2 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6abe576c081909231dc0d7304b9a9 completed April 20, 2026, 10:42 p.m.
Created at: April 16, 2026, 11:42 a.m.