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.