Triple

T530966
Position Surface form Disambiguated ID Type / Status
Subject Beijing Subway E12220 entity
Predicate hasLine P35 FINISHED
Object Line 15
Line 15 is a rapid transit line of the Beijing Subway system serving northern parts of the city with both urban and suburban stations.
 E69212 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 15 | Statement: [Beijing Subway, hasLine, Line 15]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 15
Context triple: [Beijing Subway, hasLine, Line 15]
  • A. Line 14
    Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
  • B. Line 13
    Line 13 is a suburban loop line of the Beijing Subway that serves the northern part of the city and connects several major transfer stations.
  • C. 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.
  • D. Line 9
    Line 9 is a rapid transit line of the Beijing Subway system that serves as part of the city's urban rail network.
  • E. 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.
  • 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 15
Triple: [Beijing Subway, hasLine, Line 15]
Generated description
Line 15 is a rapid transit line of the Beijing Subway system serving northern parts of the city with both urban and suburban stations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 15
Target entity description: Line 15 is a rapid transit line of the Beijing Subway system serving northern parts of the city with both urban and suburban stations.
  • A. Line 14
    Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
  • B. Line 13
    Line 13 is a suburban loop line of the Beijing Subway that serves the northern part of the city and connects several major transfer stations.
  • C. 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.
  • D. Line 9
    Line 9 is a rapid transit line of the Beijing Subway system that serves as part of the city's urban rail network.
  • E. 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.
  • 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_69a4933208e88190891f5debab1b776d completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a494dda58c8190870305056838a2b2 completed March 1, 2026, 7:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4e3f1e5908190850594ccb37f364a completed March 2, 2026, 1:12 a.m.
NEDg Description generation batch_69a4e45cc06c8190901ab3d5182be133 completed March 2, 2026, 1:14 a.m.
NED2 Entity disambiguation (via description) batch_69a4e4b1ea148190a160dc43727a281a completed March 2, 2026, 1:15 a.m.
Created at: March 1, 2026, 7:32 p.m.