Sampsonievsky
E1041841
Sampsonievsky is a municipal settlement located within the Vyborgsky District of Saint Petersburg, Russia.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Sampsonievsky canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T13129747 — 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: Sampsonievsky Context triple: [Vyborgsky District, containsSettlement, Sampsonievsky]
-
A.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
B.
Paveletskaya
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
-
C.
Artyomovsky
Artyomovsky is a town in Russia’s Ural region known for its industrial base and role as a local administrative center.
-
D.
Kamenskiy
Kamenskiy is a Slavic surname, commonly transliterated from Russian or related languages, borne by various individuals across Eastern Europe and the former Soviet Union.
-
E.
Skobelevskaya
Skobelevskaya is a Moscow Metro station serving the Severnoye Butovo District in the south of Moscow.
- 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: Sampsonievsky Target entity description: Sampsonievsky is a municipal settlement located within the Vyborgsky District of Saint Petersburg, Russia.
-
A.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
B.
Paveletskaya
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
-
C.
Artyomovsky
Artyomovsky is a town in Russia’s Ural region known for its industrial base and role as a local administrative center.
-
D.
Kamenskiy
Kamenskiy is a Slavic surname, commonly transliterated from Russian or related languages, borne by various individuals across Eastern Europe and the former Soviet Union.
-
E.
Skobelevskaya
Skobelevskaya is a Moscow Metro station serving the Severnoye Butovo District in the south of Moscow.
- F. None of above. chosen
Statements (15)
| Predicate | Object |
|---|---|
| instanceOf | municipal settlement ⓘ |
| continent | Europe ⓘ |
| country | Russia ⓘ |
| federalSubject |
Saint Petersburg
ⓘ
linked to:
Saint Petersburg Federal City
|
| geographicLocation | northwestern Russia ⓘ |
| hasLocalGovernment | municipal administration ⓘ |
| hasMunicipalStatus | municipal settlement ⓘ |
| hasSettlementType | urban-type locality ⓘ |
| languageUsed | Russian ⓘ |
| locatedInAdministrativeTerritorialEntity |
Saint Petersburg
ⓘ
linked to:
Saint Petersburg Federal City
Vyborgsky District ⓘ |
| locatedInCountrySubdivision |
federal city of Saint Petersburg
ⓘ
linked to:
Saint Petersburg Federal City
|
| locatedInTimeZone | Moscow Time ⓘ |
| partOf |
Vyborgsky District of Saint Petersburg
ⓘ
linked to:
Vyborgsky District, Saint Petersburg
urban area of Saint Petersburg ⓘ |
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: Sampsonievsky Description of subject: Sampsonievsky is a municipal settlement located within the Vyborgsky District of Saint Petersburg, Russia.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.