Triple

T18462464
Position Surface form Disambiguated ID Type / Status
Subject Laurent El Ghaoui E451071 entity
Predicate hasPublication P80 FINISHED
Object Robust Optimization: Theory and Applications 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: Robust Optimization: Theory and Applications | Statement: [Laurent El Ghaoui, hasPublication, Robust Optimization: Theory and Applications]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robust Optimization: Theory and Applications
Context triple: [Laurent El Ghaoui, hasPublication, Robust Optimization: Theory and Applications]
  • A. Combinatorial Optimization: Algorithms and Complexity
    Combinatorial Optimization: Algorithms and Complexity is a foundational textbook that systematically develops the theory and algorithms of combinatorial optimization, emphasizing computational complexity and algorithmic efficiency.
  • B. Convex Optimization
    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.
  • C. Gomory cuts in integer programming
    Gomory cuts in integer programming are a class of cutting-plane techniques that iteratively refine linear programming relaxations to find optimal integer solutions to mixed-integer optimization problems.
  • D. Optimization over Time
    "Optimization over Time" is a seminal work by Peter Whittle that develops mathematical methods for making optimal sequential decisions in dynamic and stochastic systems.
  • E. Risk-Sensitive Optimal Control
    Risk-Sensitive Optimal Control is a foundational work in control theory that develops methods for designing controllers that explicitly account for uncertainty and variability in system performance.
  • 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: Robust Optimization: Theory and Applications
Target entity description: "Robust Optimization: Theory and Applications" is a scholarly work that develops the mathematical foundations of robust optimization and demonstrates their use in designing decision-making models that remain effective under uncertainty.
  • A. Combinatorial Optimization: Algorithms and Complexity
    Combinatorial Optimization: Algorithms and Complexity is a foundational textbook that systematically develops the theory and algorithms of combinatorial optimization, emphasizing computational complexity and algorithmic efficiency.
  • B. Convex Optimization
    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.
  • C. Gomory cuts in integer programming
    Gomory cuts in integer programming are a class of cutting-plane techniques that iteratively refine linear programming relaxations to find optimal integer solutions to mixed-integer optimization problems.
  • D. Optimization over Time
    "Optimization over Time" is a seminal work by Peter Whittle that develops mathematical methods for making optimal sequential decisions in dynamic and stochastic systems.
  • E. Risk-Sensitive Optimal Control
    Risk-Sensitive Optimal Control is a foundational work in control theory that develops methods for designing controllers that explicitly account for uncertainty and variability in system performance.
  • 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_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.