Triple
T4541999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhen Metro |
E107554
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 11
Line 11 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, providing high-speed urban and airport rail service across key districts.
|
E452472
|
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 11 | Statement: [Shenzhen Metro, hasLine, Line 11]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 11 Context triple: [Shenzhen Metro, hasLine, Line 11]
-
A.
Line 11
Line 11 is a short, automated light metro line in the Barcelona Metro network that serves the hilly northern suburbs of the city.
-
B.
Line 11
Line 11 is a major Shanghai Metro route known for its long cross-city alignment connecting suburban areas with central Shanghai and serving key commercial and residential districts.
-
C.
Line 10
Line 10 is a major loop line of the Beijing Subway that encircles central urban districts and serves as a key transfer route in the network.
-
D.
Line 10
Line 10 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key residential and commercial districts.
-
E.
Line 10
Line 10 is a trolleybus route within Geneva’s public transport system that connects key districts and suburbs of 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 11 Triple: [Shenzhen Metro, hasLine, Line 11]
Generated description
Line 11 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, providing high-speed urban and airport rail service across key districts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 11 Target entity description: Line 11 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, providing high-speed urban and airport rail service across key districts.
-
A.
Line 11
Line 11 is a major Shanghai Metro route known for its long cross-city alignment connecting suburban areas with central Shanghai and serving key commercial and residential districts.
-
B.
Line 11
Line 11 is a short, automated light metro line in the Barcelona Metro network that serves the hilly northern suburbs of the city.
-
C.
Line 10
Line 10 is a major loop line of the Beijing Subway that encircles central urban districts and serves as a key transfer route in the network.
-
D.
Line 10
Line 10 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key residential and commercial districts.
-
E.
Line 10
Line 10 is a major Shanghai Metro route known for serving central districts and key hubs such as Hongqiao Transportation Hub and the city’s historic and commercial areas.
- 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_69bdc55ae8248190acda4f10eb5ce2e7 |
completed | March 20, 2026, 10:08 p.m. |
| NEDg | Description generation | batch_69bdc758f0288190ad57ec8ef5786c66 |
completed | March 20, 2026, 10:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdc7d126fc819094a97fe155d267dd |
completed | March 20, 2026, 10:18 p.m. |
Created at: March 20, 2026, 1:04 p.m.