Jure Leskovec
E356904
Jure Leskovec is a prominent computer scientist known for his influential work in data mining, social network analysis, and machine learning, particularly on large-scale graph data.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Jure Leskovec canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T3425613 — 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: Jure Leskovec Context triple: [SIGKDD Innovation Award, notableRecipient, Jure Leskovec]
-
A.
Denny Vrandečić
Denny Vrandečić is a Croatian computer scientist and Wikimedian best known for founding Wikidata and proposing the Wikifunctions project.
-
B.
Daphne Koller
Daphne Koller is a computer scientist and entrepreneur best known as the co-founder of the online education platform Coursera and for her influential work in probabilistic graphical models and machine learning.
-
C.
Andrew B. Moore
Andrew B. Moore was an American politician who served as governor of Alabama in the years leading up to and during the early part of the Civil War.
-
D.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
-
E.
Michael P. Kearns
Michael P. Kearns is an American politician from New York who has served in various local and state offices, including roles in the New York State Assembly and Erie County government.
- 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: Jure Leskovec Target entity description: Jure Leskovec is a prominent computer scientist known for his influential work in data mining, social network analysis, and machine learning, particularly on large-scale graph data.
-
A.
Denny Vrandečić
Denny Vrandečić is a Croatian computer scientist and Wikimedian best known for founding Wikidata and proposing the Wikifunctions project.
-
B.
Daphne Koller
Daphne Koller is a computer scientist and entrepreneur best known as the co-founder of the online education platform Coursera and for her influential work in probabilistic graphical models and machine learning.
-
C.
Andrew B. Moore
Andrew B. Moore was an American politician who served as governor of Alabama in the years leading up to and during the early part of the Civil War.
-
D.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
-
E.
Michael P. Kearns
Michael P. Kearns is an American politician from New York who has served in various local and state offices, including roles in the New York State Assembly and Erie County government.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
professor ⓘ researcher ⓘ |
| almaMater |
Carnegie Mellon University
ⓘ
linked to:
CMU
University of Ljubljana ⓘ |
| basedIn |
California
ⓘ
linked to:
California, United States
Stanford, California ⓘ |
| coAuthor |
Anand Rajaraman
ⓘ
Christos Faloutsos ⓘ Devi Parikh ⓘ Jeff Ullman ⓘ Jon Kleinberg ⓘ Lars Backstrom ⓘ |
| countryOfCitizenship | Slovenia ⓘ |
| doctoralAdvisor | Christos Faloutsos ⓘ |
| employer | Stanford University ⓘ |
| fieldOfDoctoralThesis |
data mining
ⓘ
graph mining ⓘ |
| fieldOfWork |
computer science
ⓘ
data mining ⓘ data science ⓘ graph mining ⓘ machine learning ⓘ network science ⓘ social network analysis ⓘ |
| hasRole | co-author of the textbook Mining of Massive Datasets ⓘ |
| knownFor |
graph-based machine learning methods
ⓘ
large-scale graph analysis ⓘ social network analysis research ⓘ work on information diffusion in networks ⓘ work on network sampling and modeling ⓘ |
| memberOf |
Stanford AI Lab
ⓘ
linked to:
Stanford Artificial Intelligence Laboratory
Stanford InfoLab ⓘ
linked to:
Stanford Computer Science Department
|
| nativeLanguage | Slovene ⓘ |
| notableWork |
Mining of Massive Datasets
ⓘ
research on community detection in large networks ⓘ research on network cascades and contagion ⓘ work on graph-based recommender systems ⓘ |
| occupation | associate professor of computer science at Stanford University ⓘ |
| researchInterest |
computational social science
ⓘ
graph representation learning ⓘ information diffusion ⓘ large-scale data analysis ⓘ network modeling ⓘ social media analysis ⓘ web mining ⓘ |
| workInstitution |
Department of Computer Science, Stanford University
ⓘ
linked to:
Stanford Computer Science Department
|
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: Jure Leskovec Description of subject: Jure Leskovec is a prominent computer scientist known for his influential work in data mining, social network analysis, and machine learning, particularly on large-scale graph data.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.