RSMC Toulouse
E227510
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RSMC Toulouse canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T1744620 — 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 Toulouse Context triple: [Regional Specialized Meteorological Centres, hasMember, RSMC Toulouse]
-
A.
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.
-
B.
RSMC Exeter
RSMC Exeter is a specialized World Meteorological Organization center based in Exeter, UK, responsible for providing expert meteorological services and guidance, particularly for high-impact weather events.
-
C.
RSMC La Réunion
RSMC La Réunion is a World Meteorological Organization-designated center responsible for monitoring and forecasting tropical cyclones in the southwest Indian Ocean.
-
D.
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.
-
E.
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.
- 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 Toulouse Target entity description: 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.
-
A.
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.
-
B.
RSMC Exeter
RSMC Exeter is a specialized World Meteorological Organization center based in Exeter, UK, responsible for providing expert meteorological services and guidance, particularly for high-impact weather events.
-
C.
RSMC La Réunion
RSMC La Réunion is a World Meteorological Organization-designated center responsible for monitoring and forecasting tropical cyclones in the southwest Indian Ocean.
-
D.
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.
-
E.
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.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
Regional Specialized Meteorological Centre
ⓘ
meteorological centre ⓘ |
| abbreviation | RSMC Toulouse self-link ⓘ |
| affiliation |
Météo-France
ⓘ
surface form:
Météo-France Direction des Opérations
|
| coordinatesWith |
International Atomic Energy Agency
ⓘ
other WMO Regional Specialized Meteorological Centres ⓘ |
| country | France ⓘ |
| dataFormat |
GRIB
ⓘ
NetCDF ⓘ |
| designatedBy | World Meteorological Organization ⓘ |
| focusesOn |
chemical accident emergency response support
ⓘ
environmental emergencies ⓘ nuclear accident emergency response support ⓘ volcanic ash dispersion forecasting ⓘ |
| governedBy | WMO regulations ⓘ |
| hasParentOrganization |
WMO regional associations
ⓘ
surface form:
World Meteorological Organization regional network
|
| hasRole |
provision of atmospheric transport and dispersion products
ⓘ
provision of regional meteorological guidance ⓘ |
| hasWebsite | https://www.meteo.fr (Météo-France portal including RSMC information) ⓘ |
| languageOfWork |
English
ⓘ
French ⓘ |
| locatedIn | Toulouse ⓘ |
| mandate | support international emergency response with specialized forecasts ⓘ |
| operatedBy | Météo-France ⓘ |
| partOf |
Global Data-processing and Forecasting System
ⓘ
surface form:
WMO Global Data-processing and Forecasting System
WMO emergency response activities ⓘ |
| provides |
concentration forecasts
ⓘ
deposition forecasts ⓘ trajectory forecasts ⓘ |
| providesServiceTo |
WMO Member States
ⓘ
surface form:
WMO Members
national meteorological and hydrological services ⓘ |
| regionServed |
Europe
ⓘ
Mediterranean Basin ⓘ
surface form:
Mediterranean region
Atlantic Ocean ⓘ
surface form:
North Atlantic
|
| sector |
environmental protection
ⓘ
meteorology ⓘ |
| specializesIn |
atmospheric dispersion forecasting
ⓘ
environmental emergency response support ⓘ regional meteorological forecasting ⓘ |
| typeOfEmergencySupported |
chemical releases
ⓘ
nuclear emergencies ⓘ radiological emergencies ⓘ volcanic eruptions ⓘ |
| usesModel |
atmospheric transport and dispersion models
ⓘ
numerical weather prediction models ⓘ |
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 Toulouse Description of subject: 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.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.