STM
E85453
STM is the public transit agency serving the city of Montreal, operating its bus and metro networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| STM canonical | 13 |
How this entity was disambiguated
This entity first appeared as the object of triple T694647 — 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: STM Context triple: [Société de transport de Montréal, shortName, STM]
-
A.
ST
ST is the commonly used abbreviation for SkyTeam, a major global airline alliance.
-
B.
STE
STE is the vehicle registration code assigned to the district of Lichtenfels in Germany.
-
C.
NST
NST is the standard time abbreviation for the Newfoundland Time Zone, which is uniquely offset by 3.5 hours from Coordinated Universal Time (UTC−3:30).
-
D.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
E.
SL
The Mercedes-Benz SL is a long-running line of luxury grand touring roadsters renowned for combining high performance with elegant design and advanced technology.
- 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: STM Target entity description: STM is the public transit agency serving the city of Montreal, operating its bus and metro networks.
-
A.
ST
ST is the commonly used abbreviation for SkyTeam, a major global airline alliance.
-
B.
STE
STE is the vehicle registration code assigned to the district of Lichtenfels in Germany.
-
C.
NST
NST is the standard time abbreviation for the Newfoundland Time Zone, which is uniquely offset by 3.5 hours from Coordinated Universal Time (UTC−3:30).
-
D.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
E.
SL
The Mercedes-Benz SL is a long-running line of luxury grand touring roadsters renowned for combining high performance with elegant design and advanced technology.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
public transit agency
ⓘ
transport operator ⓘ |
| collaboratesWith | Autorité régionale de transport métropolitain ⓘ |
| country | Canada ⓘ |
| fareSystem | integrated fare system ⓘ |
| focusesOn |
public transit development
ⓘ
sustainable mobility ⓘ |
| governedBy | board of directors ⓘ |
| hasAbbreviationMeaning | Société de transport de Montréal ⓘ |
| hasCustomerServiceChannel |
call centre
ⓘ
mobile application ⓘ online information ⓘ |
| hasNetwork |
Montreal Metro network
ⓘ
linked to:
Montreal Metro
STM bus network ⓘ |
| hasRollingStockType |
diesel buses
ⓘ
electric buses ⓘ hybrid buses ⓘ rubber-tyred metro trains ⓘ |
| hasSafetyProgram | public transit security measures ⓘ |
| hasServiceCharacteristic |
fixed-route service
ⓘ
scheduled service ⓘ |
| headquartersLocation | Montreal ⓘ |
| implements |
accessibility measures
ⓘ
real-time information systems ⓘ |
| industry | public transportation ⓘ |
| legalForm | public corporation ⓘ |
| locatedInTimeZone | Eastern Time Zone ⓘ |
| modeOfTransport |
bus
ⓘ
metro ⓘ |
| officialLanguage | French ⓘ |
| operates |
Montreal Metro
ⓘ
bus network ⓘ |
| operatesIn |
Montreal metropolitan area
ⓘ
linked to:
Montreal region
Quebec ⓘ
linked to:
Quebec, Canada
|
| owner |
City of Montreal
ⓘ
linked to:
Montreal
|
| providesService |
public bus service
ⓘ
rapid transit service ⓘ |
| regionServed |
Greater Montreal
ⓘ
linked to:
Montreal region
|
| serves |
Island of Montreal
ⓘ
Montreal ⓘ |
| serviceType |
mass transit
ⓘ
urban transit ⓘ |
| usesTicketingSystem |
contactless smart card
ⓘ
paper tickets ⓘ |
| websiteLanguage |
English
ⓘ
French ⓘ |
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: STM Description of subject: STM is the public transit agency serving the city of Montreal, operating its bus and metro networks.
Referenced by (13)
Full triples — surface form annotated when it differs from this entity's canonical label.