MESA
E300735
MESA is a large, long-term medical research study that investigates the prevalence, risk factors, and progression of cardiovascular disease in a diverse, multi-ethnic population.
All labels observed (1)
| Label | Occurrences |
|---|---|
| MESA canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T2814931 — 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: MESA Context triple: [Multi-Ethnic Study of Atherosclerosis, abbreviation, MESA]
-
A.
Mesa
Mesa is a pioneering systems programming language developed at Xerox PARC in the 1970s, notable for its strong typing, modularity, and influence on later languages and operating system design.
-
B.
Mesa, Arizona
Mesa, Arizona is a large city in the Phoenix metropolitan area known for its desert climate, suburban communities, and role as a major spring training hub for Major League Baseball.
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C.
Mesa Central
Mesa Central is a high, semi-arid plateau region in central Mexico known for its basins, volcanic ranges, and significant agricultural and urban centers.
-
D.
Martinez
Martinez is a common Spanish-origin surname widely borne across the Spanish-speaking world and beyond.
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E.
Mission Valley
Mission Valley is a major commercial and residential neighborhood in central San Diego known for its shopping centers, hotels, and proximity to key freeways and the San Diego River.
- 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: MESA Target entity description: MESA is a large, long-term medical research study that investigates the prevalence, risk factors, and progression of cardiovascular disease in a diverse, multi-ethnic population.
-
A.
Mesa
Mesa is a pioneering systems programming language developed at Xerox PARC in the 1970s, notable for its strong typing, modularity, and influence on later languages and operating system design.
-
B.
Mesa, Arizona
Mesa, Arizona is a large city in the Phoenix metropolitan area known for its desert climate, suburban communities, and role as a major spring training hub for Major League Baseball.
-
C.
Mesa Central
Mesa Central is a high, semi-arid plateau region in central Mexico known for its basins, volcanic ranges, and significant agricultural and urban centers.
-
D.
Martinez
Martinez is a common Spanish-origin surname widely borne across the Spanish-speaking world and beyond.
-
E.
Mission Valley
Mission Valley is a major commercial and residential neighborhood in central San Diego known for its shopping centers, hotels, and proximity to key freeways and the San Diego River.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
cardiovascular disease study
ⓘ
longitudinal cohort study ⓘ medical research study ⓘ |
| acronym | MESA self-link ⓘ |
| ageRangeOfParticipants | middle-aged and older adults ⓘ |
| collectsDataOn |
biomarkers
ⓘ
cardiac structure and function ⓘ carotid intima-media thickness ⓘ coronary artery calcium ⓘ genetic factors ⓘ lifestyle factors ⓘ traditional cardiovascular risk factors ⓘ |
| country |
United States of America
ⓘ
surface form:
United States
|
| dataType |
clinical data
ⓘ
imaging data ⓘ laboratory data ⓘ questionnaire data ⓘ |
| design | prospective cohort study ⓘ |
| focusesOn |
prevalence of cardiovascular disease
ⓘ
progression of cardiovascular disease ⓘ risk factors for cardiovascular disease ⓘ subclinical cardiovascular disease ⓘ |
| fullName | Multi-Ethnic Study of Atherosclerosis ⓘ |
| funder | National Institutes of Health ⓘ |
| hasCohortSize | over 6000 participants ⓘ |
| hasOutcome |
cardiovascular events
ⓘ
cardiovascular mortality ⓘ myocardial infarction ⓘ stroke ⓘ |
| includesEthnicGroup |
Black Americans
ⓘ
surface form:
African American
Chinese Americans ⓘ
surface form:
Chinese American
Hispanic ⓘ White ⓘ |
| isUsedFor |
population-based cardiovascular research
ⓘ
risk prediction model development ⓘ validation of cardiovascular risk scores ⓘ |
| method |
cardiac magnetic resonance imaging
ⓘ
computed tomography of coronary arteries ⓘ imaging of coronary arteries ⓘ long-term follow-up ⓘ ultrasound of carotid arteries ⓘ |
| objective |
to improve prediction of cardiovascular events
ⓘ
to study ethnic differences in cardiovascular disease ⓘ to understand early stages of atherosclerosis ⓘ |
| populationType |
community-based cohort
ⓘ
multi-ethnic population ⓘ |
| researchArea |
atherosclerosis
ⓘ
cardiovascular disease ⓘ epidemiology ⓘ preventive cardiology ⓘ |
| sponsor | National Heart, Lung, and Blood Institute ⓘ |
| startYear | 2000 ⓘ |
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: MESA Description of subject: MESA is a large, long-term medical research study that investigates the prevalence, risk factors, and progression of cardiovascular disease in a diverse, multi-ethnic population.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.