commune of Le Kremlin-Bicêtre
E635099
The commune of Le Kremlin-Bicêtre is a suburban municipality just south of Paris, France, known for its dense urban character and proximity to the capital.
All labels observed (1)
| Label | Occurrences |
|---|---|
| commune of Le Kremlin-Bicêtre canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T6987584 — 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: commune of Le Kremlin-Bicêtre Context triple: [arrondissement of L’Haÿ-les-Roses, contains, commune of Le Kremlin-Bicêtre]
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A.
commune of Neuilly-sur-Seine
The commune of Neuilly-sur-Seine is an affluent suburban municipality just west of central Paris, known for its upscale residential neighborhoods, corporate headquarters, and proximity to the Bois de Boulogne and La Défense business district.
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B.
Commune of Épinay-sur-Seine
The Commune of Épinay-sur-Seine is a suburban municipality in the northern outskirts of Paris, France, known for its residential neighborhoods and location along the Seine River.
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C.
commune of Vitry-sur-Seine
The commune of Vitry-sur-Seine is a populous suburban municipality in the southeastern outskirts of Paris, France, known for its urban character and proximity to the capital.
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D.
commune of Villeneuve-le-Roi
The commune of Villeneuve-le-Roi is a suburban municipality in the southeastern outskirts of Paris, France, situated along the Seine River and known for its proximity to Orly Airport.
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E.
Commune of Gagny
The Commune of Gagny is a suburban town in the eastern outskirts of Paris, France, known for its residential character and location within the Île-de-France 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: commune of Le Kremlin-Bicêtre Target entity description: The commune of Le Kremlin-Bicêtre is a suburban municipality just south of Paris, France, known for its dense urban character and proximity to the capital.
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A.
commune of Neuilly-sur-Seine
The commune of Neuilly-sur-Seine is an affluent suburban municipality just west of central Paris, known for its upscale residential neighborhoods, corporate headquarters, and proximity to the Bois de Boulogne and La Défense business district.
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B.
Commune of Épinay-sur-Seine
The Commune of Épinay-sur-Seine is a suburban municipality in the northern outskirts of Paris, France, known for its residential neighborhoods and location along the Seine River.
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C.
commune of Vitry-sur-Seine
The commune of Vitry-sur-Seine is a populous suburban municipality in the southeastern outskirts of Paris, France, known for its urban character and proximity to the capital.
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D.
commune of Villeneuve-le-Roi
The commune of Villeneuve-le-Roi is a suburban municipality in the southeastern outskirts of Paris, France, situated along the Seine River and known for its proximity to Orly Airport.
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E.
Commune of Gagny
The Commune of Gagny is a suburban town in the eastern outskirts of Paris, France, known for its residential character and location within the Île-de-France region.
- F. None of above. chosen
Statements (47)
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: commune of Le Kremlin-Bicêtre Description of subject: The commune of Le Kremlin-Bicêtre is a suburban municipality just south of Paris, France, known for its dense urban character and proximity to the capital.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.