Châtillon-Montrouge depot
E753529
Châtillon-Montrouge depot is a major Paris Métro maintenance and storage facility, notably serving rolling stock such as the MF 77 trains.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Châtillon-Montrouge depot canonical | 1 |
| Paris Métro depots | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8731379 — 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: Châtillon-Montrouge depot Context triple: [MF 77, depot, Châtillon-Montrouge depot]
-
A.
Fontenay-sous-Bois depot
Fontenay-sous-Bois depot is a maintenance and storage facility serving trains on Paris Métro Line 1, located in the eastern suburb of Fontenay-sous-Bois.
-
B.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
C.
Sceaux RER station
Sceaux RER station is a commuter rail stop on the RER B line serving the suburban town of Sceaux in the southern outskirts of Paris, France.
-
D.
Vanves–Malakoff station
Vanves–Malakoff station is a suburban railway station in the southwestern Paris metropolitan area serving local commuter traffic on the Transilien network.
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E.
Chardon-Lagache station
Chardon-Lagache station is a Paris Métro station in the 16th arrondissement, serving the residential Auteuil area.
- 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: Châtillon-Montrouge depot Target entity description: Châtillon-Montrouge depot is a major Paris Métro maintenance and storage facility, notably serving rolling stock such as the MF 77 trains.
-
A.
Fontenay-sous-Bois depot
Fontenay-sous-Bois depot is a maintenance and storage facility serving trains on Paris Métro Line 1, located in the eastern suburb of Fontenay-sous-Bois.
-
B.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
C.
Sceaux RER station
Sceaux RER station is a commuter rail stop on the RER B line serving the suburban town of Sceaux in the southern outskirts of Paris, France.
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D.
Vanves–Malakoff station
Vanves–Malakoff station is a suburban railway station in the southwestern Paris metropolitan area serving local commuter traffic on the Transilien network.
-
E.
Chardon-Lagache station
Chardon-Lagache station is a Paris Métro station in the 16th arrondissement, serving the residential Auteuil area.
- F. None of above. chosen
Statements (29)
| Predicate | Object |
|---|---|
| instanceOf |
Paris Métro depot
ⓘ
railway maintenance facility ⓘ |
| country | France ⓘ |
| hasAccessType | non-public facility ⓘ |
| hasInfrastructureType |
maintenance halls
ⓘ
rail yard ⓘ workshops ⓘ |
| hasLanguageOfWorkOrName | French ⓘ |
| locatedIn |
Paris metropolitan area
NERFINISHED
ⓘ
southern suburbs of Paris ⓘ Île-de-France region ⓘ
surface form:
Île-de-France
|
| locatedNear |
Châtillon
NERFINISHED
ⓘ
Montrouge NERFINISHED ⓘ |
| networkTypeServed | rapid transit ⓘ |
| operator |
RATP group
ⓘ
surface form:
RATP
|
| partOf |
Paris Métro infrastructure
NERFINISHED
ⓘ
RATP maintenance system NERFINISHED ⓘ |
| primaryFunction |
maintenance of rolling stock
ⓘ
stabling of rolling stock ⓘ |
| servesNetwork | Paris Métro NERFINISHED ⓘ |
| servesRollingStockType |
MF 77
NERFINISHED
ⓘ
rubber-tyred metro trains ⓘ |
| servesTransportMode | metro ⓘ |
| usedFor |
cleaning of trains
ⓘ
heavy maintenance ⓘ inspection of rolling stock ⓘ light maintenance ⓘ overnight storage of trains ⓘ repair of trains ⓘ |
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: Châtillon-Montrouge depot Description of subject: Châtillon-Montrouge depot is a major Paris Métro maintenance and storage facility, notably serving rolling stock such as the MF 77 trains.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.