Triple
T4541992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhen Metro |
E107554
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 4
Line 4 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's major north–south corridors.
|
E451278
|
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 4 | Statement: [Shenzhen Metro, hasLine, Line 4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 4 Context triple: [Shenzhen Metro, hasLine, Line 4]
-
A.
Line 4
Line 4 is a major line of the Santiago Metro in Chile, serving key residential and commercial areas in the southeastern part of the city.
-
B.
Line 4
Line 4 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving key urban and suburban areas along its north–south corridor.
-
C.
Line 4
Line 4 is one of the main lines of the Tehran Metro rapid transit system, serving key east–west corridors across Iran’s capital city.
-
D.
Line 4
Line 4 is a circular rapid transit route of the Shanghai Metro system that loops around central districts and provides key transfer connections across the network.
-
E.
Line 4
Line 4 is one of the main north–south lines of the Paris Métro, known for serving central Paris and connecting key railway stations and neighborhoods.
- 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 4 Triple: [Shenzhen Metro, hasLine, Line 4]
Generated description
Line 4 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's major north–south corridors.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 4 Target entity description: Line 4 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's major north–south corridors.
-
A.
Line 4
Line 4 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving key urban and suburban areas along its north–south corridor.
-
B.
Line 4
Line 4 is a major north–south rapid transit route in the Beijing Subway system, serving key commercial, residential, and university areas of the city.
-
C.
Line 4
Line 4 is a circular rapid transit route of the Shanghai Metro system that loops around central districts and provides key transfer connections across the network.
-
D.
Line 4
Line 4 is one of the main north–south lines of the Paris Métro, known for serving central Paris and connecting key railway stations and neighborhoods.
-
E.
Line 4
Line 4 is a planned rapid transit route within the future Ho Chi Minh City Metro system in Vietnam.
- 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.