Triple

T7384717
Position Surface form Disambiguated ID Type / Status
Subject Nanjing Metro E170351 entity
Predicate hasLine P35 FINISHED
Object Line 10
Line 10 is a rapid transit line of the Nanjing Metro system in Nanjing, China, serving as part of the city's urban rail network.
 E660752 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 10 | Statement: [Nanjing Metro, hasLine, Line 10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 10
Context triple: [Nanjing Metro, hasLine, Line 10]
  • A. 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.
  • B. 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.
  • C. Line 10
    Line 10 is a trolleybus route within Geneva’s public transport system that connects key districts and suburbs of the city.
  • 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 11
    Line 11 is a short, automated light metro line in the Barcelona Metro network that serves the hilly northern 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 10
Triple: [Nanjing Metro, hasLine, Line 10]
Generated description
Line 10 is a rapid transit line of the Nanjing Metro system in Nanjing, China, serving as part of the city's urban rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 10
Target entity description: Line 10 is a rapid transit line of the Nanjing Metro system in Nanjing, China, serving as part of the city's urban rail network.
  • A. 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.
  • B. 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.
  • C. Line 10
    Line 10 is a trolleybus route within Geneva’s public transport system that connects key districts and suburbs of the city.
  • 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 11
    Line 11 is a short, automated light metro line in the Barcelona Metro network that serves the hilly northern suburbs of the city.
  • 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_69c68a5d0ed08190b6d361e68f813330 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f1efe1308190b96eefbff56140be completed March 27, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c802e23714819094a1b31c82a27fee completed March 28, 2026, 4:33 p.m.
NEDg Description generation batch_69c8038127408190947cb7002ccc0dec completed March 28, 2026, 4:36 p.m.
NED2 Entity disambiguation (via description) batch_69c8040a40088190b37192429678fd3e completed March 28, 2026, 4:38 p.m.
Created at: March 27, 2026, 3:08 p.m.