Triple

T18462422
Position Surface form Disambiguated ID Type / Status
Subject Lieven Vandenberghe E451070 entity
Predicate notableWork P4 FINISHED
Object Convex Optimization — NE NERFINISHED

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: Convex Optimization | Statement: [Lieven Vandenberghe, notableWork, Convex Optimization]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Convex Optimization
Context triple: [Lieven Vandenberghe, notableWork, Convex Optimization]
  • A. Convex Optimization chosen
    Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
  • B. Nonlinear programming
    Nonlinear programming is a branch of mathematical optimization focused on finding optimal solutions to problems where the objective function or constraints are nonlinear.
  • C. 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.
  • D. Karush–Kuhn–Tucker conditions
    The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
  • E. Slater’s condition
    Slater’s condition is a regularity condition in convex optimization that guarantees strong duality and the validity of the Karush–Kuhn–Tucker optimality conditions by requiring the existence of a strictly feasible point.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d8d38345688190b565eac2e4cd7935 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e52a80a2bc81909ec14811577a311d completed April 19, 2026, 7:18 p.m.
Created at: April 10, 2026, 11:33 a.m.