Triple

T19531998
Position Surface form Disambiguated ID Type / Status
Subject Coding and Information Theory E488677 entity
Predicate relatedWork P37 FINISHED
Object Information Theory and Reliable Communication — 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: Information Theory and Reliable Communication | Statement: [Coding and Information Theory, relatedWork, Information Theory and Reliable Communication]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Information Theory and Reliable Communication
Context triple: [Coding and Information Theory, relatedWork, Information Theory and Reliable Communication]
  • A. Elements of Information Theory
    Elements of Information Theory is a foundational textbook that systematically develops the theory and applications of information theory, widely used in communications, coding, and data science.
  • B. Mathematical Foundations of Information Theory
    Mathematical Foundations of Information Theory is a seminal monograph by Aleksandr Khinchin that rigorously develops the probabilistic and mathematical basis of Shannon’s information theory.
  • C. An Introduction to Information Theory: Symbols, Signals and Noise
    An Introduction to Information Theory: Symbols, Signals and Noise is a classic, accessible textbook that explains the fundamental concepts of information theory, communication, and coding for a broad scientific and engineering audience.
  • D. A Mathematical Theory of Communication
    A Mathematical Theory of Communication is Claude Shannon’s landmark 1948 paper that founded information theory by rigorously defining concepts like information, entropy, and channel capacity.
  • E. Slepian–Wolf coding theorem
    The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
  • 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: Information Theory and Reliable Communication
Target entity description: "Information Theory and Reliable Communication" is a foundational textbook by Robert G. Gallager that rigorously develops the principles of information theory and their application to designing reliable communication systems.
  • A. Elements of Information Theory
    Elements of Information Theory is a foundational textbook that systematically develops the theory and applications of information theory, widely used in communications, coding, and data science.
  • B. Mathematical Foundations of Information Theory
    Mathematical Foundations of Information Theory is a seminal monograph by Aleksandr Khinchin that rigorously develops the probabilistic and mathematical basis of Shannon’s information theory.
  • C. An Introduction to Information Theory: Symbols, Signals and Noise
    An Introduction to Information Theory: Symbols, Signals and Noise is a classic, accessible textbook that explains the fundamental concepts of information theory, communication, and coding for a broad scientific and engineering audience.
  • D. A Mathematical Theory of Communication
    A Mathematical Theory of Communication is Claude Shannon’s landmark 1948 paper that founded information theory by rigorously defining concepts like information, entropy, and channel capacity.
  • E. Slepian–Wolf coding theorem
    The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
  • 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_69d8e8db5b6c8190984b61f91981f575 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6363fd1f8819080805346efad2579 completed April 20, 2026, 2:20 p.m.
Created at: April 10, 2026, 1:41 p.m.