Triple

T6639765
Position Surface form Disambiguated ID Type / Status
Subject Lausanne Métro E150556 entity
Predicate hasLine P35 FINISHED
Object M2 line
The M2 line is a fully automated metro line in Lausanne, Switzerland, running on a steep north–south route that connects the city center with surrounding districts and the lakeshore.
 E609382 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: M2 line | Statement: [Lausanne Métro, hasLine, M2 line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: M2 line
Context triple: [Lausanne Métro, hasLine, M2 line]
  • A. M2 line
    The M2 line is one of the main lines of the Helsinki Metro rapid transit system, serving key districts across the Helsinki metropolitan area.
  • B. M2 line
    The M2 line is a major rapid transit route within the Ankara Metro system in Turkey, serving key districts of the capital city.
  • C. M3 line
    The M3 line is a rapid transit route within the Ankara Metro system serving parts of Turkey’s capital city.
  • D. M1 line
    The M1 line is a light metro route in Lausanne, Switzerland, connecting the city center with the university and lakeside areas as part of the Lausanne Métro network.
  • E. M1 line
    The M1 line is a primary rapid transit route of the Ankara Metro system serving key districts of Turkey’s capital 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: M2 line
Triple: [Lausanne Métro, hasLine, M2 line]
Generated description
The M2 line is a fully automated metro line in Lausanne, Switzerland, running on a steep north–south route that connects the city center with surrounding districts and the lakeshore.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: M2 line
Target entity description: The M2 line is a fully automated metro line in Lausanne, Switzerland, running on a steep north–south route that connects the city center with surrounding districts and the lakeshore.
  • A. M2 line
    The M2 line is a major rapid transit route within the Ankara Metro system in Turkey, serving key districts of the capital city.
  • B. M2 line
    The M2 line is one of the main lines of the Helsinki Metro rapid transit system, serving key districts across the Helsinki metropolitan area.
  • C. M3 line
    The M3 line is a rapid transit route within the Ankara Metro system serving parts of Turkey’s capital city.
  • D. M1 line
    The M1 line is a primary rapid transit route of the Ankara Metro system serving key districts of Turkey’s capital city.
  • E. M1 line
    The M1 line is one of the main lines of the Helsinki Metro, running east–west through the Helsinki region and serving several key suburban and central stations.
  • 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_69c687f0ceb08190bf40807bfc605fa5 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6aff1fe8081908c32db341b0fb354 completed March 27, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6eeed01608190bbc6461ac8c3cadb completed March 27, 2026, 8:56 p.m.
NEDg Description generation batch_69c6f0a1149c8190af55a613eada84b6 completed March 27, 2026, 9:03 p.m.
NED2 Entity disambiguation (via description) batch_69c6f17ccd7c8190918e03b114f4f064 completed March 27, 2026, 9:07 p.m.
Created at: March 27, 2026, 2 p.m.