TVM-300
E408790
TVM-300 is a high-speed railway cab signalling and train control system used on French high-speed lines such as the LGV Nord.
All labels observed (1)
| Label | Occurrences |
|---|---|
| TVM-300 canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T3997921 — 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: TVM-300 Context triple: [LGV Nord, hasSignallingSystem, TVM-300]
-
A.
TVM-430
TVM-430 is a modern in-cab railway signaling and train protection system used on high-speed lines such as the French TGV network.
-
B.
TX-30
TX-30 is the commonly used abbreviation for Texas's 30th congressional district, a U.S. House of Representatives district centered in the Dallas area.
-
C.
TX-38
TX-38 is a U.S. congressional district in Texas represented in the House of Representatives.
-
D.
TX-35
TX-35 is the commonly used abbreviation for Texas's 35th congressional district, a U.S. House of Representatives district centered around parts of Austin and San Antonio.
-
E.
TX-20
TX-20 is a United States congressional district centered on San Antonio, Texas, known for its strong Democratic lean and significant Hispanic population.
- 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: TVM-300 Target entity description: TVM-300 is a high-speed railway cab signalling and train control system used on French high-speed lines such as the LGV Nord.
-
A.
TVM-430
TVM-430 is a modern in-cab railway signaling and train protection system used on high-speed lines such as the French TGV network.
-
B.
TX-30
TX-30 is the commonly used abbreviation for Texas's 30th congressional district, a U.S. House of Representatives district centered in the Dallas area.
-
C.
TX-38
TX-38 is a U.S. congressional district in Texas represented in the House of Representatives.
-
D.
TX-35
TX-35 is the commonly used abbreviation for Texas's 35th congressional district, a U.S. House of Representatives district centered around parts of Austin and San Antonio.
-
E.
TX-20
TX-20 is a United States congressional district centered on San Antonio, Texas, known for its strong Democratic lean and significant Hispanic population.
- F. None of above. chosen
Statements (39)
| Predicate | Object |
|---|---|
| instanceOf |
cab signalling system
ⓘ
railway signalling system ⓘ train control system ⓘ |
| application | dedicated high-speed lines ⓘ |
| compatibleRollingStock |
TGV Atlantique
ⓘ
linked to:
TGV Atlantique trainset
TGV Sud-Est ⓘ
linked to:
TGV PSE
|
| controlType | continuous automatic train control ⓘ |
| countryOfUse | France ⓘ |
| designedFor | high-speed rail ⓘ |
| developer |
Alstom
ⓘ
SNCF ⓘ |
| displayLocation | driver’s cab ⓘ |
| informationTransmission | track-to-train ⓘ |
| informationTransmissionMedium | coded track circuits ⓘ |
| introducedInDecade | 1980s ⓘ |
| lineSideSignalsRequired | no ⓘ |
| maximumSpeedSupported | 300 km/h ⓘ |
| operatingPrinciple | moving block-like fixed blocks ⓘ |
| originCountry | France ⓘ |
| providesToDriver |
block occupancy status
ⓘ
braking curve information ⓘ permissible speed ⓘ |
| region | Western Europe ⓘ |
| replacedBy | TVM-430 ⓘ |
| safetyFunction |
enforcement of signal aspects
ⓘ
overspeed protection ⓘ prevention of rear-end collisions ⓘ |
| signalIndicationForm | numeric speed codes ⓘ |
| speedSupervision | on-board computer ⓘ |
| successorSystem | TVM-430 ⓘ |
| supportsBrakingControl | stepwise speed codes ⓘ |
| usedByOperator |
Eurostar
ⓘ
SNCF ⓘ |
| usedFor | high-speed passenger services ⓘ |
| usedOnLine |
LGV Atlantique
ⓘ
LGV Méditerranée ⓘ LGV Nord ⓘ LGV Rhône-Alpes ⓘ LGV Sud-Est ⓘ |
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: TVM-300 Description of subject: TVM-300 is a high-speed railway cab signalling and train control system used on French high-speed lines such as the LGV Nord.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.