Triple

T16570821
Position Surface form Disambiguated ID Type / Status
Subject TGV Lyria E402577 entity
Predicate primaryRoute P6298 FINISHED
Object Paris–Lausanne
Paris–Lausanne is an international high-speed rail route linking the French capital Paris with the Swiss city of Lausanne.
 E1222806 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: Paris–Lausanne | Statement: [TGV Lyria, primaryRoute, Paris–Lausanne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Paris–Lausanne
Context triple: [TGV Lyria, primaryRoute, Paris–Lausanne]
  • A. Paris–Geneva
    Paris–Geneva is a major international high-speed rail route linking the French capital with the Swiss city of Geneva.
  • B. Paris–Basel
    Paris–Basel is an international air route linking the French capital Paris with the Swiss city of Basel.
  • C. Lausanne
    Lausanne is a major Swiss city on the shores of Lake Geneva, known for hosting the International Olympic Committee and its vibrant cultural and academic institutions.
  • D. Nyon
    Nyon is a Swiss town on the shores of Lake Geneva that serves as the administrative home of several major sports organizations, including UEFA.
  • E. Lausanne-Ouchy
    Lausanne-Ouchy is the lakeside district and former fishing village of Lausanne on Lake Geneva, known today as a scenic waterfront promenade and transport hub with parks, hotels, and ferry connections.
  • 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: Paris–Lausanne
Triple: [TGV Lyria, primaryRoute, Paris–Lausanne]
Generated description
Paris–Lausanne is an international high-speed rail route linking the French capital Paris with the Swiss city of Lausanne.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Paris–Lausanne
Target entity description: Paris–Lausanne is an international high-speed rail route linking the French capital Paris with the Swiss city of Lausanne.
  • A. Paris–Geneva
    Paris–Geneva is a major international high-speed rail route linking the French capital with the Swiss city of Geneva.
  • B. Paris–Basel
    Paris–Basel is an international air route linking the French capital Paris with the Swiss city of Basel.
  • C. Lausanne
    Lausanne is a major Swiss city on the shores of Lake Geneva, known for hosting the International Olympic Committee and its vibrant cultural and academic institutions.
  • D. Nyon
    Nyon is a Swiss town on the shores of Lake Geneva that serves as the administrative home of several major sports organizations, including UEFA.
  • E. Lausanne-Ouchy
    Lausanne-Ouchy is the lakeside district and former fishing village of Lausanne on Lake Geneva, known today as a scenic waterfront promenade and transport hub with parks, hotels, and ferry connections.
  • 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_69d8838648088190acf97ef11fc3f61b completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35958d49c8190b995188240fb355b completed April 18, 2026, 10:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00759424348190889dacbbc7435238 completed May 10, 2026, 12:09 p.m.
NEDg Description generation batch_6a00783334a08190ae76fd7e114a9dd6 completed May 10, 2026, 12:21 p.m.
NED2 Entity disambiguation (via description) batch_6a0078562e788190b80a4ee27788ff69 completed May 10, 2026, 12:21 p.m.
Created at: April 10, 2026, 5:16 a.m.