Clifford Stein
E181897
Clifford Stein is a computer scientist and professor best known for his contributions to algorithms and operations research, including co-authoring the widely used textbook "Introduction to Algorithms."
All labels observed (1)
| Label | Occurrences |
|---|---|
| Clifford Stein canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T1531352 — 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: Clifford Stein Context triple: [Introduction to Algorithms, hasAuthor, Clifford Stein]
-
A.
Thomas Blatt
Thomas Blatt was a Polish Jewish Holocaust survivor, writer, and speaker best known for his escape from the Sobibor extermination camp and his later efforts to document its history.
-
B.
Rich Kleiman
Rich Kleiman is an American sports agent and entrepreneur best known as Kevin Durant’s longtime business partner and co-founder of the sports and entertainment company Boardroom and the investment firm Thirty Five Ventures.
-
C.
Daniel H. Weiss
Daniel H. Weiss is an American art historian and academic leader who served as president and CEO of New York’s Metropolitan Museum of Art.
-
D.
Richard Lipton
Richard Lipton is an American computer scientist known for his influential work in theoretical computer science and cryptography, including contributions to complexity theory and algorithm design.
-
E.
Raymond G. Perelman
Raymond G. Perelman was an American businessman and philanthropist known for his major charitable contributions to education, medicine, and the arts, particularly at the University of Pennsylvania.
- 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: Clifford Stein Target entity description: Clifford Stein is a computer scientist and professor best known for his contributions to algorithms and operations research, including co-authoring the widely used textbook "Introduction to Algorithms."
-
A.
Thomas Blatt
Thomas Blatt was a Polish Jewish Holocaust survivor, writer, and speaker best known for his escape from the Sobibor extermination camp and his later efforts to document its history.
-
B.
Rich Kleiman
Rich Kleiman is an American sports agent and entrepreneur best known as Kevin Durant’s longtime business partner and co-founder of the sports and entertainment company Boardroom and the investment firm Thirty Five Ventures.
-
C.
Daniel H. Weiss
Daniel H. Weiss is an American art historian and academic leader who served as president and CEO of New York’s Metropolitan Museum of Art.
-
D.
Richard Lipton
Richard Lipton is an American computer scientist known for his influential work in theoretical computer science and cryptography, including contributions to complexity theory and algorithm design.
-
E.
Raymond G. Perelman
Raymond G. Perelman was an American businessman and philanthropist known for his major charitable contributions to education, medicine, and the arts, particularly at the University of Pennsylvania.
- F. None of above. chosen
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
academic
ⓘ
author ⓘ computer scientist ⓘ person ⓘ |
| academicDiscipline |
industrial engineering
ⓘ
operations research ⓘ |
| areaOfExpertise |
algorithm analysis
ⓘ
algorithm design ⓘ combinatorial optimization ⓘ scheduling algorithms ⓘ |
| citizenship |
United States of America
ⓘ
surface form:
United States
|
| coAuthorOf |
Introduction to Algorithms
ⓘ
surface form:
"Introduction to Algorithms"
|
| coAuthorWith |
Charles E. Leiserson
ⓘ
Ronald L. Rivest ⓘ Thomas H. Cormen ⓘ |
| countryOfWork |
United States of America
ⓘ
surface form:
United States
|
| employer | Columbia University ⓘ |
| fieldOfWork |
algorithms
ⓘ
computer science ⓘ operations research ⓘ theoretical computer science ⓘ |
| hasRole |
textbook author
ⓘ
university professor ⓘ |
| hasWritten |
research papers on approximation algorithms
ⓘ
research papers on network flows ⓘ research papers on scheduling ⓘ |
| influenced | computer science education in algorithms ⓘ |
| knownFor |
algorithms research
ⓘ
approximation algorithms ⓘ co-authoring "Introduction to Algorithms" ⓘ operations research contributions ⓘ scheduling theory ⓘ |
| languageOfWorkOrName | English ⓘ |
| notableFor | co-authoring a standard algorithms textbook used worldwide ⓘ |
| notableWork |
Introduction to Algorithms
ⓘ
surface form:
"Introduction to Algorithms"
research on approximation algorithms ⓘ |
| occupation |
author
ⓘ
professor ⓘ researcher ⓘ |
| teaches |
algorithms
ⓘ
operations research ⓘ |
| workInstitution | Columbia University ⓘ |
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: Clifford Stein Description of subject: Clifford Stein is a computer scientist and professor best known for his contributions to algorithms and operations research, including co-authoring the widely used textbook "Introduction to Algorithms."
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.