Triple

T3528793
Position Surface form Disambiguated ID Type / Status
Subject Lausanne E74605 entity
Predicate hasMetroLine P17559 FINISHED
Object M2
M2 is a metro line in Lausanne, Switzerland, forming part of the city's urban rapid transit network.
 E365743 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 | Statement: [Lausanne, hasMetroLine, M2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: M2
Context triple: [Lausanne, hasMetroLine, M2]
  • A. M2
    M2 is a boat line that operates as part of Geneva’s public transport network, providing passenger services across the city’s waters.
  • B. M2
    M2 is a major British motorway that connects London with the port town of Dover in Kent, serving as an important route to the Channel ports.
  • C. M2
    M2 was the original name of MTV2, a U.S. cable television channel that focused on music videos and youth-oriented programming.
  • D. M2
    M2 is the second line of the Warsaw Metro, running east–west across the city and connecting key districts on both sides of the Vistula River.
  • E. M2
    M2 is one of the main lines of the Copenhagen Metro, connecting central Copenhagen with key districts and the airport.
  • 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
Triple: [Lausanne, hasMetroLine, M2]
Generated description
M2 is a metro line in Lausanne, Switzerland, forming part of the city's urban rapid transit network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: M2
Target entity description: M2 is a metro line in Lausanne, Switzerland, forming part of the city's urban rapid transit network.
  • A. M2
    M2 is a major British motorway that connects London with the port town of Dover in Kent, serving as an important route to the Channel ports.
  • B. M2
    M2 is a boat line that operates as part of Geneva’s public transport network, providing passenger services across the city’s waters.
  • C. M2
    M2 is the second line of the Warsaw Metro, running east–west across the city and connecting key districts on both sides of the Vistula River.
  • D. M2
    M2 was the original name of MTV2, a U.S. cable television channel that focused on music videos and youth-oriented programming.
  • E. M2
    M2 is one of the main lines of the Copenhagen Metro, connecting central Copenhagen with key districts and the airport.
  • 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_69ad85d1a3948190931fd1ea1f49717b completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc6e9188819093480b39f263ce75 completed March 8, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e93c1988190a9ab7698bf63e8e6 completed March 13, 2026, 3:03 a.m.
NEDg Description generation batch_69b380a6b6ec8190be0741cb9535b650 completed March 13, 2026, 3:12 a.m.
NED2 Entity disambiguation (via description) batch_69b3812927e48190a84f3c7fa55d070a completed March 13, 2026, 3:14 a.m.
Created at: March 8, 2026, 3:19 p.m.