Triple

T8926559
Position Surface form Disambiguated ID Type / Status
Subject Abraham Wald E212550 entity
Predicate notableWork P4 FINISHED
Object Statistical Decision Functions E212552 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: Statistical Decision Functions | Statement: [Abraham Wald, notableWork, Statistical Decision Functions]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Statistical Decision Functions
Context triple: [Abraham Wald, notableWork, Statistical Decision Functions]
  • A. Statistical Decision Functions chosen
    Statistical Decision Functions is a foundational work in decision theory and statistics that systematically develops the theory of optimal decision-making under uncertainty.
  • B. Neyman–Pearson theory of hypothesis testing
    The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
  • C. Foundations of a General Theory of Sequential Decision Functions
    Foundations of a General Theory of Sequential Decision Functions is a seminal work in statistics that established the mathematical foundations of sequential analysis and optimal decision-making under uncertainty.
  • D. complete class theorem in decision theory
    The complete class theorem in decision theory is a foundational result that characterizes optimal decision rules by showing that any admissible rule belongs to a "complete class" beyond which no better procedures exist.
  • E. Sequential Analysis
    Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
  • 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_69ca839481d48190b42b037e0d0f636c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6671557c81909f3837ffd6a15ffe completed April 1, 2026, 12:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba58e9ec81909141c516d05ac790 completed April 3, 2026, 1:02 p.m.
Created at: March 30, 2026, 6:57 p.m.