Triple

T22887756
Position Surface form Disambiguated ID Type / Status
Subject Bonn public transport network E567650 entity
Predicate hasLightRailLine P473 FINISHED
Object Line 16
Line 16 is a light rail route in the Bonn public transport system that connects the city with surrounding areas as part of its regional tram and Stadtbahn network.
 E1559883 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 16 | Statement: [Bonn public transport network, hasLightRailLine, Line 16]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 16
Context triple: [Bonn public transport network, hasLightRailLine, Line 16]
  • A. Line 16
    Line 16 is a rapid transit line of the Beijing Subway system serving parts of the city with modern, high-capacity metro service.
  • B. Line 16
    Line 16 is a suburban rapid transit line of the Shanghai Metro that connects central Shanghai with the southeastern outskirts, including the Lingang area.
  • C. Line 16
    Line 16 is a suburban rapid transit line of the Hangzhou Metro system in Hangzhou, China, connecting the urban network with outlying districts.
  • D. Line 16
    Line 16 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China.
  • E. Line 16
    Line 16 is a planned automated metro line in the Grand Paris Express project intended to improve rapid transit connectivity in the eastern suburbs of the Paris metropolitan area.
  • 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 16
Triple: [Bonn public transport network, hasLightRailLine, Line 16]
Generated description
Line 16 is a light rail route in the Bonn public transport system that connects the city with surrounding areas as part of its regional tram and Stadtbahn network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 16
Target entity description: Line 16 is a light rail route in the Bonn public transport system that connects the city with surrounding areas as part of its regional tram and Stadtbahn network.
  • A. Line 16
    Line 16 is a rapid transit line of the Beijing Subway system serving parts of the city with modern, high-capacity metro service.
  • B. Line 16
    Line 16 is a suburban rapid transit line of the Shanghai Metro that connects central Shanghai with the southeastern outskirts, including the Lingang area.
  • C. Line 16
    Line 16 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China.
  • D. Line 16
    Line 16 is a suburban rapid transit line of the Hangzhou Metro system in Hangzhou, China, connecting the urban network with outlying districts.
  • E. Line 16
    Line 16 is a planned automated metro line in the Grand Paris Express project intended to improve rapid transit connectivity in the eastern suburbs of the Paris metropolitan area.
  • 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_69e2458a92ec81908fc1cd5f6407d2ab completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17fc2adb4819081bce7e6849ba31a completed April 29, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bb9be4c688190820b484bea11696b completed May 19, 2026, 1:15 a.m.
NEDg Description generation batch_6a0bbb38da508190bc1d9bc8538cdbe2 completed May 19, 2026, 1:22 a.m.
NED2 Entity disambiguation (via description) batch_6a0bbc1bd0808190a0a5f8f603308b45 completed May 19, 2026, 1:25 a.m.
Created at: April 17, 2026, 3:40 p.m.