Zipf's law
E595317
Zipf's law is an empirical statistical principle observing that in many datasets, such as word frequencies in natural language, the frequency of an item is inversely proportional to its rank in a frequency table.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Zipf's law canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T6411965 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zipf's law Context triple: [George K. Zipf, hasConceptNamedAfter, Zipf's law]
-
A.
Szemerényi's law
Szemerényi's law is a sound law in Proto-Indo-European linguistics that explains the loss of certain final consonants with compensatory lengthening of the preceding vowel.
-
B.
Zipf
Zipf is a surname most notably associated with linguist George Kingsley Zipf, known for formulating Zipf's law about word frequency distributions.
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C.
Newcomb–Benford law
The Newcomb–Benford law is a statistical principle stating that in many naturally occurring datasets, the leading digits are distributed logarithmically, with smaller digits (especially 1) appearing as the first digit more frequently than larger ones.
-
D.
Pareto distribution
The Pareto distribution is a power-law probability distribution often used to model phenomena with heavy tails and strong inequality, such as wealth or city sizes.
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E.
Pareto principle
The Pareto principle is an economic and management concept stating that roughly 80% of effects come from 20% of causes, often used to prioritize efforts and resources.
- 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: Zipf's law Target entity description: Zipf's law is an empirical statistical principle observing that in many datasets, such as word frequencies in natural language, the frequency of an item is inversely proportional to its rank in a frequency table.
-
A.
Szemerényi's law
Szemerényi's law is a sound law in Proto-Indo-European linguistics that explains the loss of certain final consonants with compensatory lengthening of the preceding vowel.
-
B.
Zipf
Zipf is a surname most notably associated with linguist George Kingsley Zipf, known for formulating Zipf's law about word frequency distributions.
-
C.
Newcomb–Benford law
The Newcomb–Benford law is a statistical principle stating that in many naturally occurring datasets, the leading digits are distributed logarithmically, with smaller digits (especially 1) appearing as the first digit more frequently than larger ones.
-
D.
Pareto distribution
The Pareto distribution is a power-law probability distribution often used to model phenomena with heavy tails and strong inequality, such as wealth or city sizes.
-
E.
Pareto principle
The Pareto principle is an economic and management concept stating that roughly 80% of effects come from 20% of causes, often used to prioritize efforts and resources.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
empirical law
ⓘ
power law ⓘ statistical law ⓘ |
| appliesTo |
city size distributions
ⓘ
income distributions ⓘ internet traffic ⓘ natural language text ⓘ population of cities ⓘ scientific paper citations ⓘ web site popularity ⓘ word frequency distributions ⓘ |
| approximateDate | 1940s ⓘ |
| category |
empirical statistical laws
ⓘ
laws of linguistics ⓘ |
| coreStatement | the frequency of an item is inversely proportional to its rank ⓘ |
| describes |
heavy-tailed distributions
ⓘ
scale-free behavior ⓘ |
| explainedBy | principle of least effort ⓘ |
| field |
complex systems
ⓘ
information theory ⓘ quantitative linguistics ⓘ statistical linguistics ⓘ |
| hasGeneralization |
Zipf–Mandelbrot law
NERFINISHED
ⓘ
discrete Pareto distribution ⓘ |
| mathematicalForm |
f(r) = C / r^s
ⓘ
f(r) ∝ 1/r ⓘ |
| namedAfter | George Kingsley Zipf NERFINISHED ⓘ |
| originatedBy | George Kingsley Zipf NERFINISHED ⓘ |
| property |
approximate rather than exact
ⓘ
empirically observed ⓘ scale invariant form ⓘ |
| publicationContext | Human Behavior and the Principle of Least Effort NERFINISHED ⓘ |
| relatedTo |
Benford's law
NERFINISHED
ⓘ
Heaps' law NERFINISHED ⓘ Pareto distribution NERFINISHED ⓘ power-law distribution ⓘ rank-size rule ⓘ |
| typicalExponent | s ≈ 1 ⓘ |
| usedIn |
computational linguistics
ⓘ
corpus linguistics ⓘ data compression ⓘ economics ⓘ information retrieval ⓘ language modeling ⓘ natural language processing ⓘ network science ⓘ urban studies ⓘ |
| variable |
frequency
ⓘ
rank ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Zipf's law Description of subject: Zipf's law is an empirical statistical principle observing that in many datasets, such as word frequencies in natural language, the frequency of an item is inversely proportional to its rank in a frequency table.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.