Triple

T2043266
Position Surface form Disambiguated ID Type / Status
Subject LGV Sud-Est E44791 entity
Predicate usedByService P1294 FINISHED
Object TGV Paris–Nice
TGV Paris–Nice is a high-speed French train service connecting Paris with the Mediterranean city of Nice.
 E227672 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: TGV Paris–Nice | Statement: [LGV Sud-Est, usedByService, TGV Paris–Nice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TGV Paris–Nice
Context triple: [LGV Sud-Est, usedByService, TGV Paris–Nice]
  • A. Paris–Nice
    Paris–Nice is a prestigious early-season professional road cycling stage race in France, often seen as a key preparation event for the Grand Tours.
  • B. Paris–Bordeaux
    Paris–Bordeaux is a major high-speed rail corridor in France connecting the capital with the southwest, known for its fast TGV services.
  • C. Bordeaux–Toulouse–Marseille
    Bordeaux–Toulouse–Marseille is a major French intercity rail corridor linking the Atlantic city of Bordeaux with Toulouse and the Mediterranean port of Marseille.
  • D. Paris–Clermont-Ferrand
    Paris–Clermont-Ferrand is a major French intercity rail route linking the capital Paris with the central city of Clermont-Ferrand.
  • E. Paris–Lille
    Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
  • 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: TGV Paris–Nice
Triple: [LGV Sud-Est, usedByService, TGV Paris–Nice]
Generated description
TGV Paris–Nice is a high-speed French train service connecting Paris with the Mediterranean city of Nice.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TGV Paris–Nice
Target entity description: TGV Paris–Nice is a high-speed French train service connecting Paris with the Mediterranean city of Nice.
  • A. Paris–Nice
    Paris–Nice is a prestigious early-season professional road cycling stage race in France, often seen as a key preparation event for the Grand Tours.
  • B. Paris–Bordeaux
    Paris–Bordeaux is a major high-speed rail corridor in France connecting the capital with the southwest, known for its fast TGV services.
  • C. Bordeaux–Toulouse–Marseille
    Bordeaux–Toulouse–Marseille is a major French intercity rail corridor linking the Atlantic city of Bordeaux with Toulouse and the Mediterranean port of Marseille.
  • D. Paris–Clermont-Ferrand
    Paris–Clermont-Ferrand is a major French intercity rail route linking the capital Paris with the central city of Clermont-Ferrand.
  • E. Paris–Lille
    Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
  • 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_69a889159ec481908f9e4472d9f480c7 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb96f932881908bebfc4176fda7c0 completed March 7, 2026, 5:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1ffe98248190b6a4428c6c094d35 completed March 9, 2026, 1:18 a.m.
NEDg Description generation batch_69ae204fe6148190915219beb27128bc completed March 9, 2026, 1:20 a.m.
NED2 Entity disambiguation (via description) batch_69ae20d09c748190aebbfb88f0eedbaa completed March 9, 2026, 1:22 a.m.
Created at: March 4, 2026, 7:39 p.m.