SMND
E335778
SMND is the station code for the central Paris RER railway station Saint-Michel–Notre-Dame, a major hub near Notre-Dame Cathedral.
All labels observed (1)
| Label | Occurrences |
|---|---|
| SMND canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T3206183 — 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: SMND Context triple: [Saint-Michel–Notre-Dame, hasStationCode, SMND]
-
A.
SMR
SMR is the three-letter ISO 3166-1 alpha-3 country code assigned to San Marino.
-
B.
SMN2
SMN2 is a human gene that produces a backup form of survival motor neuron protein and is a key therapeutic target in spinal muscular atrophy.
-
C.
MS
MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
-
D.
MS
MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
-
E.
MS
MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
- 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: SMND Target entity description: SMND is the station code for the central Paris RER railway station Saint-Michel–Notre-Dame, a major hub near Notre-Dame Cathedral.
-
A.
SMR
SMR is the three-letter ISO 3166-1 alpha-3 country code assigned to San Marino.
-
B.
SMN2
SMN2 is a human gene that produces a backup form of survival motor neuron protein and is a key therapeutic target in spinal muscular atrophy.
-
C.
MS
MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
-
D.
MS
MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
-
E.
MS
MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
- F. None of above. chosen
Statements (34)
| Predicate | Object |
|---|---|
| instanceOf |
RER station code
ⓘ
railway station code ⓘ |
| appliesTo |
Gare de Saint-Michel–Notre-Dame
ⓘ
surface form:
Saint-Michel–Notre-Dame railway station
|
| associatedWithArea | central Paris ⓘ |
| associatedWithLandmark | Notre-Dame Cathedral ⓘ |
| codeForSystem |
RER B station coding
ⓘ
RER C station coding ⓘ |
| connectsTo | Paris Métro network (via nearby stations) ⓘ |
| fareAuthority | Île-de-France Mobilités ⓘ |
| fareSystem | Île-de-France public transport ⓘ |
| hasPartInName |
Our Lady (Notre-Dame)
ⓘ
surface form:
Notre-Dame
Saint-Michel ⓘ |
| languageOfAcronym | French ⓘ |
| locatedInCity | Paris ⓘ |
| locatedInCountry | France ⓘ |
| locatedInDistrict |
5th arrondissement of Paris
ⓘ
6th arrondissement of Paris NERFINISHED ⓘ |
| locatedNear |
Notre-Dame Cathedral
ⓘ
Île de la Cité ⓘ |
| network |
RER network
ⓘ
surface form:
RER
Réseau Express Régional ⓘ |
| refersTo | Saint-Michel–Notre-Dame ⓘ |
| role | major transport hub in central Paris ⓘ |
| servesRailLine |
RER B line
ⓘ
surface form:
RER B
RER C ⓘ |
| stationType |
RER station
ⓘ
underground railway station ⓘ |
| usedByOperator |
RATP group
ⓘ
surface form:
RATP
SNCF ⓘ |
| usedInPassengerInformation |
departure boards
ⓘ
route maps ⓘ ticketing systems ⓘ |
| usedInTimetables |
RER B timetables
ⓘ
RER C timetables ⓘ |
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: SMND Description of subject: SMND is the station code for the central Paris RER railway station Saint-Michel–Notre-Dame, a major hub near Notre-Dame Cathedral.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.