Triple

T11438098
Position Surface form Disambiguated ID Type / Status
Subject Andrew Yao E271060 entity
Predicate notableConcept P201 FINISHED
Object Yao’s minimax principle E926123 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: Yao’s minimax principle | Statement: [Andrew Yao, notableConcept, Yao’s minimax principle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yao’s minimax principle
Context triple: [Andrew Yao, notableConcept, Yao’s minimax principle]
  • A. Yao’s minimax principle chosen
    Yao’s minimax principle is a fundamental result in computational complexity and randomized algorithms that relates the performance of randomized algorithms to the performance of deterministic algorithms against a worst-case input distribution.
  • B. Probably Approximately Correct learning (PAC learning)
    Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
  • C. Furst–Saxe–Sipser lower bounds
    Furst–Saxe–Sipser lower bounds are foundational results in circuit complexity theory that established superpolynomial lower bounds for constant-depth Boolean circuits (AC⁰), demonstrating inherent limitations of such circuits for computing certain functions.
  • D. Blum complexity measures
    Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
  • E. Papadimitriou: Computational Complexity
    "Papadimitriou: Computational Complexity" is a widely used graduate-level textbook that systematically develops the theory of computational complexity, including classes like P and NP and the foundations of NP-completeness.
  • 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_69d6aadeef688190874bcecd88b3dd9b completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8088711ec8190afae9f4d9f2a11ca completed April 9, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e8ed579c8190b1ddc1dce20d9617 completed April 20, 2026, 8:50 a.m.
Created at: April 8, 2026, 9:35 p.m.