Triple
T4541990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhen Metro |
E107554
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 2
Line 2 is a major east–west rapid transit route in the Shenzhen Metro system, connecting key commercial and residential districts across the city.
|
E451277
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Line 2 | Statement: [Shenzhen Metro, hasLine, Line 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 2 Context triple: [Shenzhen Metro, hasLine, Line 2]
-
A.
Line 2
Line 2 is one of the main lines of the Santiago Metro in Chile, running in a generally north–south direction and serving several central and densely populated areas of the city.
-
B.
Line 2
Line 2 is a major east–west rapid transit route of the Shanghai Metro that connects key commercial, residential, and airport hubs across the city.
-
C.
Line 2
Line 2 is a trolleybus route within Geneva’s public transport system that serves as one of the city’s main electric bus lines.
-
D.
Line 2
Line 2 is a rapid transit line of the Barcelona Metro system that serves several central and northern neighborhoods of the city.
-
E.
Line 2
Line 2 is a planned second rapid transit line of the Turin Metro system in Turin, Italy, intended to expand the city's urban rail network.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Line 2 Triple: [Shenzhen Metro, hasLine, Line 2]
Generated description
Line 2 is a major east–west rapid transit route in the Shenzhen Metro system, connecting key commercial and residential districts across the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 2 Target entity description: Line 2 is a major east–west rapid transit route in the Shenzhen Metro system, connecting key commercial and residential districts across the city.
-
A.
Line 2
Line 2 is a major east–west rapid transit route of the Shanghai Metro that connects key commercial, residential, and airport hubs across the city.
-
B.
Line 2
Line 2 is a major rapid transit route of the Guangzhou Metro system that runs through key urban districts and serves as one of the network’s primary north–south corridors.
-
C.
Line 2
Line 2 is a major route of Mexico City’s Metrobús bus rapid transit system, running along key thoroughfares to connect important residential and commercial areas.
-
D.
Line 2
Line 2 is a major subway line on Toronto's Bloor–Danforth corridor, running primarily east–west across the city.
-
E.
Line 2
Line 2 is one of the principal lines of the Mexico City Metro system, running across key central and western areas of the city and serving as a major high-capacity transit corridor.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57d219d88190a67ada845323d7fb |
completed | March 20, 2026, 2:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdb926f2608190bdc6379e81358c38 |
completed | March 20, 2026, 9:16 p.m. |
| NEDg | Description generation | batch_69bdbe0b6aa88190b6e99e4be1b27935 |
completed | March 20, 2026, 9:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdbe5eda748190b6d83d5f2c73cff5 |
completed | March 20, 2026, 9:38 p.m. |
Created at: March 20, 2026, 1:04 p.m.