Class 387 Electrostar
E643307
The Class 387 Electrostar is a British dual-voltage electric multiple unit train used for high-frequency commuter and regional services on electrified routes.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Class 387 Electrostar canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7130614 — 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: Class 387 Electrostar Context triple: [London King's Cross to King's Lynn services, rollingStock, Class 387 Electrostar]
-
A.
Class 379 Electrostar
The Class 379 Electrostar is a British electric multiple-unit train type built by Bombardier for high-frequency commuter and airport services, known for its modern design and passenger comfort.
-
B.
Class 345 Aventra
The Class 345 Aventra is a modern electric multiple unit train built by Bombardier (now Alstom) for high-capacity, high-frequency commuter services on London’s Elizabeth line.
-
C.
Class 377 electric multiple unit
The Class 377 electric multiple unit is a modern British Electrostar commuter train widely used on suburban and regional services in the south of England.
-
D.
Class 730 Aventra
The Class 730 Aventra is a modern electric multiple unit train built by Alstom (originally Bombardier) for high-capacity, commuter and regional rail services on the UK network.
-
E.
Class 390 Pendolino
The Class 390 Pendolino is a high-speed tilting electric multiple unit train used by Avanti West Coast on the West Coast Main Line in the United Kingdom.
- 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: Class 387 Electrostar Target entity description: The Class 387 Electrostar is a British dual-voltage electric multiple unit train used for high-frequency commuter and regional services on electrified routes.
-
A.
Class 379 Electrostar
The Class 379 Electrostar is a British electric multiple-unit train type built by Bombardier for high-frequency commuter and airport services, known for its modern design and passenger comfort.
-
B.
Class 345 Aventra
The Class 345 Aventra is a modern electric multiple unit train built by Bombardier (now Alstom) for high-capacity, high-frequency commuter services on London’s Elizabeth line.
-
C.
Class 377 electric multiple unit
The Class 377 electric multiple unit is a modern British Electrostar commuter train widely used on suburban and regional services in the south of England.
-
D.
Class 730 Aventra
The Class 730 Aventra is a modern electric multiple unit train built by Alstom (originally Bombardier) for high-capacity, commuter and regional rail services on the UK network.
-
E.
Class 390 Pendolino
The Class 390 Pendolino is a high-speed tilting electric multiple unit train used by Avanti West Coast on the West Coast Main Line in the United Kingdom.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
dual-voltage train
ⓘ
electric multiple unit ⓘ passenger train ⓘ |
| brakeType |
electro-pneumatic
ⓘ
regenerative braking ⓘ |
| configuration | 4-car unit ⓘ |
| countryOfOrigin | United Kingdom ⓘ |
| coupling | Dellner NERFINISHED ⓘ |
| designedBy | Bombardier Transportation UK NERFINISHED ⓘ |
| designedFor |
commuter services
ⓘ
high-frequency services ⓘ regional services ⓘ |
| doorType | sliding plug doors ⓘ |
| electricSystem |
25 kV AC overhead
ⓘ
750 V DC third rail ⓘ |
| family |
Bombardier Electrostar
NERFINISHED
ⓘ
Class 375 Electrostar NERFINISHED ⓘ Class 376 Electrostar NERFINISHED ⓘ Class 377 Electrostar NERFINISHED ⓘ Class 379 Electrostar NERFINISHED ⓘ |
| floorHeight | standard British platform height ⓘ |
| hasClassDesignation | Class 387 NERFINISHED ⓘ |
| hasMultipleWorking | compatible with other Electrostar units ⓘ |
| hasSeating |
first class
ⓘ
standard class ⓘ |
| introduced | 2014 ⓘ |
| manufacturer | Bombardier Transportation NERFINISHED ⓘ |
| operatesIn | England ⓘ |
| operator |
Govia Thameslink Railway
NERFINISHED
ⓘ
Great Western Railway NERFINISHED ⓘ Heathrow Express NERFINISHED ⓘ |
| powerCollectionMethod |
pantograph
ⓘ
third-rail shoegear ⓘ |
| powerType | electric ⓘ |
| serviceType |
semi-fast services
ⓘ
stopping services ⓘ |
| topSpeed |
110 mph
ⓘ
177 km/h ⓘ |
| trackGauge | standard gauge ⓘ |
| UICClassification | Bo′Bo′+2′2′ or similar multiple-unit arrangement ⓘ |
| usedFor |
airport services
ⓘ
inter-urban services ⓘ suburban services ⓘ |
| usedOn |
Great Western Main Line
NERFINISHED
ⓘ
Heathrow Airport services ⓘ Thameslink route NERFINISHED ⓘ electrified routes ⓘ |
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: Class 387 Electrostar Description of subject: The Class 387 Electrostar is a British dual-voltage electric multiple unit train used for high-frequency commuter and regional services on electrified routes.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.