Triple

T6639776
Position Surface form Disambiguated ID Type / Status
Subject Lausanne Métro E150556 entity
Predicate hasDepot P2413 FINISHED
Object Vennes depot
Vennes depot is a maintenance and storage facility serving Lausanne’s metro system in Switzerland.
 E608299 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: Vennes depot | Statement: [Lausanne Métro, hasDepot, Vennes depot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vennes depot
Context triple: [Lausanne Métro, hasDepot, Vennes depot]
  • A. Grefsen depot
    Grefsen depot is a major tram depot in Oslo, Norway, serving as a key maintenance and storage facility for the city’s tram network.
  • B. Carnide depot
    Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • C. Vastral Depot
    Vastral Depot is a major maintenance and operations facility serving the Ahmedabad Metro system in Ahmedabad, India.
  • D. Bachet depot
    Bachet depot is a major tram maintenance and storage facility serving the public transport system in Geneva, Switzerland.
  • E. Elliniko depot
    Elliniko depot is a maintenance and storage facility serving the Athens Metro system in Athens, Greece.
  • 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: Vennes depot
Triple: [Lausanne Métro, hasDepot, Vennes depot]
Generated description
Vennes depot is a maintenance and storage facility serving Lausanne’s metro system in Switzerland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vennes depot
Target entity description: Vennes depot is a maintenance and storage facility serving Lausanne’s metro system in Switzerland.
  • A. Grefsen depot
    Grefsen depot is a major tram depot in Oslo, Norway, serving as a key maintenance and storage facility for the city’s tram network.
  • B. Carnide depot
    Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • C. Vastral Depot
    Vastral Depot is a major maintenance and operations facility serving the Ahmedabad Metro system in Ahmedabad, India.
  • D. Bachet depot
    Bachet depot is a major tram maintenance and storage facility serving the public transport system in Geneva, Switzerland.
  • E. Elliniko depot
    Elliniko depot is a maintenance and storage facility serving the Athens Metro system in Athens, Greece.
  • 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_69c6e455edb88190983f74f39e55665c completed March 27, 2026, 8:11 p.m.
NEDg Description generation batch_69c6e8584bd08190bb45747aca6e9327 completed March 27, 2026, 8:28 p.m.
NED2 Entity disambiguation (via description) batch_69c6e8dcc9bc819099ba39f67677195b completed March 27, 2026, 8:30 p.m.
Created at: March 27, 2026, 2 p.m.