Triple
T13903339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangzhou Metro |
E334282
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 5
Line 5 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as one of the city's key urban rail corridors.
|
E1070232
|
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 5 | Statement: [Hangzhou Metro, hasLine, Line 5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 5 Context triple: [Hangzhou Metro, hasLine, Line 5]
-
A.
Line 5
Line 5 is a major north–south route of the Beijing Subway known for connecting key residential and commercial areas through the city center.
-
B.
Line 5
Line 5 is a major east–west route of the Brussels Metro system, connecting key districts across the Belgian capital.
-
C.
Line 5
Line 5 is one of the routes of the Tunis Metro light rail network, serving passengers across part of the Tunis metropolitan area.
-
D.
Line 5
Line 5 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's key urban rail corridors.
-
E.
Line 5
Line 5 is one of the main lines of the Paris Métro, running in a generally north–south direction and serving several key stations and neighborhoods across the city.
- 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 5 Triple: [Hangzhou Metro, hasLine, Line 5]
Generated description
Line 5 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as one of the city's key urban rail corridors.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 5 Target entity description: Line 5 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as one of the city's key urban rail corridors.
-
A.
Line 5
Line 5 is a rapid transit line of the Chongqing Metro system in Chongqing, China, serving as one of the city's main urban rail corridors.
-
B.
Line 5
Line 5 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's key urban rail corridors.
-
C.
Line 5
Line 5 is a rapid transit line of the Shanghai Metro system serving the southern suburbs of the city.
-
D.
Line 5
Line 5 is a major east–west rapid transit route in the Guangzhou Metro system, serving key urban districts and facilitating high-capacity cross-city travel.
-
E.
Line 5
Line 5 is a planned rapid transit route of the Ho Chi Minh City Metro system intended to serve as part of the city’s future urban rail network.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25db1e308190aaed6a21e443cc44 |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce7638a88190aae1b59c00ee27ce |
completed | May 3, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69f9fd56da288190b2bd33bc496c3fb9 |
completed | May 5, 2026, 2:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fb039fdb1c8190ad5286d1cfe80a29 |
completed | May 6, 2026, 9:02 a.m. |
Created at: April 9, 2026, 10:16 p.m.