Triple
T4541991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhen Metro |
E107554
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 3
Line 3 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key urban districts along a major north–south corridor.
|
E452469
|
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 3 | Statement: [Shenzhen Metro, hasLine, Line 3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 3 Context triple: [Shenzhen Metro, hasLine, Line 3]
-
A.
Line 3
Line 3 is a rapid transit line of the Toronto subway system, commonly known as the Scarborough RT, that served the Scarborough district.
-
B.
Line 3
Line 3 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
-
C.
Line 3
Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
-
D.
Line 3
Line 3 is a major line of the Moscow Metro system, known for serving central Moscow and connecting key residential and commercial districts.
-
E.
Line 3
Line 3 is a Culver CityBus route in the Los Angeles area that connects key destinations across Culver City and nearby communities.
- 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 3 Triple: [Shenzhen Metro, hasLine, Line 3]
Generated description
Line 3 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key urban districts along a major north–south corridor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 3 Target entity description: Line 3 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key urban districts along a major north–south corridor.
-
A.
Line 3
Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
-
B.
Line 3
Line 3 is a major north–south rapid transit route of the Shanghai Metro system, known for its elevated tracks and extensive coverage across the city.
-
C.
Line 3
Line 3 is a major line of the Moscow Metro system, known for serving central Moscow and connecting key residential and commercial districts.
-
D.
Line 3
Line 3 is a major north–south route of the Tehran Metro system, connecting key residential and commercial areas across the city.
-
E.
Line 3
Line 3 is a rapid transit line of the Toronto subway system, commonly known as the Scarborough RT, that served the Scarborough district.
- 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.