RSMC Washington
E230324
RSMC Washington is a World Meteorological Organization-designated center responsible for providing specialized meteorological analysis and forecasting, particularly for tropical cyclones in the Eastern Pacific and related regions.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RSMC Washington canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T1744621 — 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: RSMC Washington Context triple: [Regional Specialized Meteorological Centres, hasMember, RSMC Washington]
-
A.
RSMC Honolulu
RSMC Honolulu is a World Meteorological Organization-designated center responsible for specialized tropical cyclone forecasting and related meteorological services in the Central Pacific region.
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B.
RSMC Montreal
RSMC Montreal is a World Meteorological Organization-designated center in Canada that provides specialized meteorological services, particularly for environmental emergencies such as atmospheric transport and dispersion of pollutants.
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C.
RSMC Nadi
RSMC Nadi is a World Meteorological Organization-designated regional center in Fiji responsible for tropical cyclone forecasting and warnings in the South Pacific region.
-
D.
RSMC Wellington
RSMC Wellington is a specialized meteorological center operated by New Zealand’s MetService that provides regional forecasting and warning services, particularly for severe weather and tropical cyclones in the South Pacific.
-
E.
RSMC Toulouse
RSMC Toulouse is a World Meteorological Organization-designated center in France that specializes in providing regional meteorological and atmospheric dispersion forecasts, particularly for environmental emergencies.
- 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: RSMC Washington Target entity description: RSMC Washington is a World Meteorological Organization-designated center responsible for providing specialized meteorological analysis and forecasting, particularly for tropical cyclones in the Eastern Pacific and related regions.
-
A.
RSMC Honolulu
RSMC Honolulu is a World Meteorological Organization-designated center responsible for specialized tropical cyclone forecasting and related meteorological services in the Central Pacific region.
-
B.
RSMC Montreal
RSMC Montreal is a World Meteorological Organization-designated center in Canada that provides specialized meteorological services, particularly for environmental emergencies such as atmospheric transport and dispersion of pollutants.
-
C.
RSMC Nadi
RSMC Nadi is a World Meteorological Organization-designated regional center in Fiji responsible for tropical cyclone forecasting and warnings in the South Pacific region.
-
D.
RSMC Wellington
RSMC Wellington is a specialized meteorological center operated by New Zealand’s MetService that provides regional forecasting and warning services, particularly for severe weather and tropical cyclones in the South Pacific.
-
E.
RSMC Toulouse
RSMC Toulouse is a World Meteorological Organization-designated center in France that specializes in providing regional meteorological and atmospheric dispersion forecasts, particularly for environmental emergencies.
- F. None of above. chosen
Statements (34)
| Predicate | Object |
|---|---|
| instanceOf |
Regional Specialized Meteorological Center
ⓘ
meteorological organization ⓘ |
| areaOfResponsibility |
Central Pacific Ocean
ⓘ
surface form:
Central North Pacific Ocean
eastern North Pacific Ocean ⓘ
surface form:
Eastern North Pacific Ocean
Atlantic Ocean ⓘ
surface form:
North Atlantic Ocean
|
| coordinatesWith |
Tropical Cyclone Warning Centers
ⓘ
other WMO Regional Specialized Meteorological Centers ⓘ |
| country |
United States of America
ⓘ
surface form:
United States
|
| dataSource |
in-situ meteorological observations
ⓘ
numerical weather prediction models ⓘ satellite observations ⓘ |
| designation | World Meteorological Organization-designated center ⓘ |
| focusesOn |
disaster risk reduction support
ⓘ
hazardous tropical weather ⓘ |
| hasAbbreviation | RSMC Washington self-link ⓘ |
| languageOfWork | English ⓘ |
| locatedIn | Washington, D.C. ⓘ |
| mainRole |
meteorological forecasting
ⓘ
specialized meteorological analysis ⓘ |
| operator |
National Centers for Environmental Prediction
ⓘ
National Oceanic and Atmospheric Administration ⓘ National Weather Service ⓘ |
| partOf |
WMO Tropical Cyclone Programme
ⓘ
surface form:
World Meteorological Organization tropical cyclone programme
|
| providesProduct |
marine weather guidance
ⓘ
tropical cyclone advisories ⓘ tropical cyclone forecasts ⓘ tropical cyclone intensity guidance ⓘ tropical cyclone track guidance ⓘ |
| providesServiceTo |
WMO Member States
ⓘ
surface form:
WMO Members
national meteorological and hydrological services ⓘ |
| reportsTo | World Meteorological Organization ⓘ |
| specializesIn |
tropical cyclone analysis
ⓘ
tropical cyclone forecasting ⓘ |
| usesStandard | World Meteorological Organization standards ⓘ |
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: RSMC Washington Description of subject: RSMC Washington is a World Meteorological Organization-designated center responsible for providing specialized meteorological analysis and forecasting, particularly for tropical cyclones in the Eastern Pacific and related regions.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.