Triple

T7115723

Position	Surface form	Disambiguated ID	Type / Status
Subject	Thomas M. Cover	`E165813`	entity
Predicate	notableWork	`P4`	FINISHED
Object	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.	`E641826`	NE FINISHED

How this triple was built (4 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: Elements of Information Theory | Statement: [Thomas M. Cover, notableWork, Elements of Information Theory]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Elements of Information Theory
Context triple: [Thomas M. Cover, notableWork, Elements of Information Theory]

A. 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.
B. 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.
C. Coding and Information Theory
"Coding and Information Theory" is a foundational textbook by Richard W. Hamming that introduces the mathematical principles underlying error-correcting codes and the transmission of information.
D. information theory
Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
E. Fano inequality
Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: Elements of Information Theory
Triple: [Thomas M. Cover, notableWork, Elements of Information Theory]

Generated description

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.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Elements of Information Theory
Target entity description: 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.

A. 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.
B. 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.
C. Coding and Information Theory
"Coding and Information Theory" is a foundational textbook by Richard W. Hamming that introduces the mathematical principles underlying error-correcting codes and the transmission of information.
D. information theory
Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
E. Fano inequality
Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
F. None of above. chosen

Provenance (5 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_69c6888227bc8190a1394679e3116f90`	completed	March 27, 2026, 1:39 p.m.
NER	Named-entity recognition	`batch_69c6e5f401b881909ef4c2ab1e0750db`	completed	March 27, 2026, 8:17 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69c79cbfc7a08190ab07f3d65aa79f16`	completed	March 28, 2026, 9:17 a.m.
NEDg	Description generation	`batch_69c79d0215888190b0e59c2584358a05`	completed	March 28, 2026, 9:18 a.m.
NED2	Entity disambiguation (via description)	`batch_69c79d63b6dc8190b3b52ef6566ba490`	completed	March 28, 2026, 9:20 a.m.

Created at: March 27, 2026, 2:43 p.m.