Triple
T8038636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Baran |
E187377
|
entity |
| Predicate | fieldOfWork |
P3
|
FINISHED |
| Object | information theory |
E158223
|
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: information theory | Statement: [Paul Baran, fieldOfWork, information theory]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: information theory Context triple: [Paul Baran, fieldOfWork, information theory]
-
A.
information theory
chosen
Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
-
B.
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.
-
C.
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.
-
D.
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.
-
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.
- 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f19a6b4819085e2887fb0c54d08 |
completed | March 31, 2026, 3:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc570090a48190b84ba8325563148e |
completed | March 31, 2026, 11:21 p.m. |
Created at: March 30, 2026, 5:23 p.m.