Triple

T18597334
Position Surface form Disambiguated ID Type / Status
Subject Aarhus Letbane E454525 entity
Predicate hasLine P35 FINISHED
Object Line L1
Line L1 is one of the light rail routes of the Aarhus Letbane system in Aarhus, Denmark.
 E1333023 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 L1 | Statement: [Aarhus Letbane, hasLine, Line L1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line L1
Context triple: [Aarhus Letbane, hasLine, Line L1]
  • A. Line L
    Line L is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the main urban transit network.
  • B. Line P
    Line P is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
  • C. Line 1
    Line 1 is a primary rapid transit route of the Shijiazhuang Metro system in Shijiazhuang, China, serving key urban areas along an east–west corridor.
  • D. Line 1
    Line 1 is a major rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving as one of the city's primary east–west corridors.
  • E. Line 1
    Line 1 is the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
  • 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 L1
Triple: [Aarhus Letbane, hasLine, Line L1]
Generated description
Line L1 is one of the light rail routes of the Aarhus Letbane system in Aarhus, Denmark.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line L1
Target entity description: Line L1 is one of the light rail routes of the Aarhus Letbane system in Aarhus, Denmark.
  • A. Line L
    Line L is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the main urban transit network.
  • B. Line P
    Line P is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
  • C. Line 1
    Line 1 is a primary rapid transit route of the Shijiazhuang Metro system in Shijiazhuang, China, serving key urban areas along an east–west corridor.
  • D. Line 1
    Line 1 is a major rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving as one of the city's primary east–west corridors.
  • E. Line 1
    Line 1 is the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
  • 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_69d8d38ae7e081908a98df1251842402 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5474ce0c08190b440cbe86b6ef7b9 completed April 19, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a05037eda788190977fcb50aa4d2357 completed May 13, 2026, 11:04 p.m.
NEDg Description generation batch_6a0504cd76388190b67c78250297573d completed May 13, 2026, 11:10 p.m.
NED2 Entity disambiguation (via description) batch_6a0505482c588190952fe07726f64b93 completed May 13, 2026, 11:12 p.m.
Created at: April 10, 2026, 11:44 a.m.