Assistance publique – Hôpitaux de Paris
E693112
Assistance publique – Hôpitaux de Paris is the public hospital system of Paris and its suburbs, and one of the largest hospital networks in Europe.
All labels observed (5)
| Label | Occurrences |
|---|---|
| Assistance Publique – Hôpitaux de Paris | 13 |
| Assistance publique – Hôpitaux de Paris canonical | 2 |
| AP-HP | 1 |
| Assistance publique – Hôpitaux de Paris network | 1 |
| Institut mutualiste Montsouris | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7777967 — 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: Assistance publique – Hôpitaux de Paris Context triple: [Hôtel-Dieu de Paris, operatedBy, Assistance publique – Hôpitaux de Paris]
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A.
Hôpital général de Paris
Hôpital général de Paris was a powerful 17th-century French institution that combined charitable, medical, and custodial functions to confine and manage the poor, sick, and socially marginalized in Paris.
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B.
Hôpital Robert-Debré, Paris
Hôpital Robert-Debré, Paris is a major pediatric teaching hospital in Paris, France, renowned for its specialized care for children and its role in medical research and education.
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C.
Hôpital de la Pitié-Salpêtrière
Hôpital de la Pitié-Salpêtrière is a major Parisian teaching hospital and medical research center renowned for its historical role in neurology and psychiatry.
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D.
Hôpital Necker-Enfants Malades
Hôpital Necker-Enfants Malades is a renowned Parisian teaching hospital particularly famous for its pioneering pediatric care and medical research.
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E.
Hôpital Saint-Louis
Hôpital Saint-Louis is a historic Parisian hospital renowned for its early 17th-century architecture and its longstanding role in treating infectious diseases and dermatological conditions.
- 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: Assistance publique – Hôpitaux de Paris Target entity description: Assistance publique – Hôpitaux de Paris is the public hospital system of Paris and its suburbs, and one of the largest hospital networks in Europe.
-
A.
Hôpital général de Paris
Hôpital général de Paris was a powerful 17th-century French institution that combined charitable, medical, and custodial functions to confine and manage the poor, sick, and socially marginalized in Paris.
-
B.
Hôpital Robert-Debré, Paris
Hôpital Robert-Debré, Paris is a major pediatric teaching hospital in Paris, France, renowned for its specialized care for children and its role in medical research and education.
-
C.
Hôpital de la Pitié-Salpêtrière
Hôpital de la Pitié-Salpêtrière is a major Parisian teaching hospital and medical research center renowned for its historical role in neurology and psychiatry.
-
D.
Hôpital Necker-Enfants Malades
Hôpital Necker-Enfants Malades is a renowned Parisian teaching hospital particularly famous for its pioneering pediatric care and medical research.
-
E.
Hôpital Saint-Louis
Hôpital Saint-Louis is a historic Parisian hospital renowned for its early 17th-century architecture and its longstanding role in treating infectious diseases and dermatological conditions.
- F. None of above. chosen
Statements (48)
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: Assistance publique – Hôpitaux de Paris Description of subject: Assistance publique – Hôpitaux de Paris is the public hospital system of Paris and its suburbs, and one of the largest hospital networks in Europe.
Referenced by (18)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
linked to: Assistance publique – Hôpitaux de Paris
subject linked to:
Hôpital Cochin (partly)
linked to: Assistance publique – Hôpitaux de Paris
subject linked to:
Hôpital Cochin (partly)
linked to: Assistance publique – Hôpitaux de Paris