Triple

T13903341
Position Surface form Disambiguated ID Type / Status
Subject Hangzhou Metro E334282 entity
Predicate hasLine P35 FINISHED
Object Line 7
Line 7 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's urban rail network.
E1068773 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 7 | Statement: [Hangzhou Metro, hasLine, Line 7]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 7
Context triple: [Hangzhou Metro, hasLine, Line 7]
  • A. Line 7
    Line 7 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
  • B. Line 7
    Line 7 is one of the main lines of the Tehran Metro rapid transit network, serving various districts of Iran’s capital city.
  • C. Line 7
    Line 7 is a rapid transit route of the STC Metro system, serving as one of its numbered lines within the network.
  • D. Line 7
    Line 7 is an east–west rapid transit line of the Beijing Subway serving several central and southwestern districts of Beijing.
  • E. Line 7
    Line 7 is a rapid transit line of the Guangzhou Metro system serving parts of Guangzhou and its surrounding areas.
  • 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 7
Triple: [Hangzhou Metro, hasLine, Line 7]
Generated description
Line 7 is a rapid transit line of the Hangzhou Metro system in Hangzhou, 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 7
Target entity description: Line 7 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's urban rail network.
  • A. Line 7
    Line 7 is a rapid transit line of the Guangzhou Metro system serving parts of Guangzhou and its surrounding areas.
  • B. Line 7
    Line 7 is a rapid transit route of the STC Metro system, serving as one of its numbered lines within the network.
  • C. Line 7
    Line 7 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving several key districts across the city.
  • D. Line 7
    Line 7 is a major rapid transit route of the Shanghai Metro that runs in a roughly north–south direction, connecting several key residential, commercial, and cultural areas across the city.
  • E. Line 7
    Line 7 is one of the main lines of the Tehran Metro rapid transit network, serving various districts of Iran’s capital 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_69d81c5eaa9c819083b1ff8689179565 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de25db1e308190aaed6a21e443cc44 completed April 14, 2026, 11:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c722e72081909090b2d64000ebd9 completed May 3, 2026, 10:07 p.m.
NEDg Description generation batch_69f7c83a3e04819097b6e0b5a3161b9a completed May 3, 2026, 10:12 p.m.
NED2 Entity disambiguation (via description) batch_69f7c9732d188190a8a7151d21e0a310 completed May 3, 2026, 10:17 p.m.
Created at: April 9, 2026, 10:16 p.m.