Triple

T22887758
Position Surface form Disambiguated ID Type / Status
Subject Bonn public transport network E567650 entity
Predicate hasLightRailLine P473 FINISHED
Object Line 63
Line 63 is a light rail route within Bonn’s Stadtbahn network that connects key districts of the city and surrounding areas.
 E1561954 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 63 | Statement: [Bonn public transport network, hasLightRailLine, Line 63]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 63
Context triple: [Bonn public transport network, hasLightRailLine, Line 63]
  • A. Line 62
    Line 62 is a tram route within Bonn’s public transport system that serves local passengers along its designated corridor in the city.
  • B. Line 66
    Line 66 is a major light rail route in Bonn’s public transport system, connecting key districts and serving as an important commuter corridor in the region.
  • C. Line 43
    Line 43 is a planned rapid transit line in the Shenzhen Metro system in Shenzhen, China.
  • D. Line 33
    Line 33 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
  • E. Line 61
    Line 61 is a tram route within Bonn’s public transport system that connects key districts 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 63
Triple: [Bonn public transport network, hasLightRailLine, Line 63]
Generated description
Line 63 is a light rail route within Bonn’s Stadtbahn network that connects key districts of the city and surrounding areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 63
Target entity description: Line 63 is a light rail route within Bonn’s Stadtbahn network that connects key districts of the city and surrounding areas.
  • A. Line 62
    Line 62 is a tram route within Bonn’s public transport system that serves local passengers along its designated corridor in the city.
  • B. Line 66
    Line 66 is a major light rail route in Bonn’s public transport system, connecting key districts and serving as an important commuter corridor in the region.
  • C. Line 43
    Line 43 is a planned rapid transit line in the Shenzhen Metro system in Shenzhen, China.
  • D. Line 33
    Line 33 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
  • E. Line 61
    Line 61 is a tram route within Bonn’s public transport system that connects key districts 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_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_6a0bc238ba6c81908ededa16b25e5d2c completed May 19, 2026, 1:51 a.m.
NEDg Description generation batch_6a0bc35c3ea08190a3b9111297774e6c completed May 19, 2026, 1:56 a.m.
NED2 Entity disambiguation (via description) batch_6a0bc43eafe081909dfff8b40d624635 completed May 19, 2026, 2 a.m.
Created at: April 17, 2026, 3:40 p.m.