Line 27
E770426
Line 27 is a planned rapid transit line of the Chongqing Metro system in Chongqing, China.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Line 27 canonical | 1 |
Statements (20)
| Predicate | Object |
|---|---|
| instanceOf | rapid transit line ⓘ |
| cityServed | Chongqing NERFINISHED ⓘ |
| continent | Asia ⓘ |
| country | China ⓘ |
| countryServed | China NERFINISHED ⓘ |
| electrification | electric (likely, not confirmed) ⓘ |
| hasTrackGauge | standard gauge (likely, not confirmed) ⓘ |
| locatedIn |
China
ⓘ
Chongqing NERFINISHED ⓘ Chongqing Municipality NERFINISHED ⓘ |
| modeOfTransport | rapid transit ⓘ |
| operator | Chongqing Rail Transit Corporation (planned) NERFINISHED ⓘ |
| partOf | Chongqing Metro NERFINISHED ⓘ |
| planned | true ⓘ |
| publicTransportType | metro ⓘ |
| railwaySignalling | metro signalling (likely, not confirmed) ⓘ |
| regionServed | Chongqing NERFINISHED ⓘ |
| status | planned ⓘ |
| system | Chongqing Metro NERFINISHED ⓘ |
| transportNetwork | Chongqing Metro NERFINISHED ⓘ |
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: Line 27 Description of subject: Line 27 is a planned rapid transit line of the Chongqing Metro system in Chongqing, China.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.