Triple

T17840328
Position Surface form Disambiguated ID Type / Status
Subject TGV E445505 entity
Predicate route P5619 FINISHED
Object Lille–Lyon
Lille–Lyon is a high-speed rail connection in France linking the northern city of Lille with Lyon, a major hub in the southeast.
E1296129 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: Lille–Lyon | Statement: [TGV, route, Lille–Lyon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lille–Lyon
Context triple: [TGV, route, Lille–Lyon]
  • A. Paris–Lille
    Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
  • B. Paris–Montpellier
    Paris–Montpellier is a major high-speed rail corridor in France linking the capital with the Mediterranean city of Montpellier.
  • C. Paris–Strasbourg
    Paris–Strasbourg is a major high-speed rail corridor in France linking the capital with the Alsatian city near the German border.
  • D. Paris–Bayonne
    Paris–Bayonne is a major French rail corridor linking the capital Paris with the southwestern city of Bayonne, serving as a key route toward the Atlantic coast and the Spanish border.
  • E. Paris–Lyon
    Paris–Lyon is the pioneering high-speed rail corridor in France that became the flagship route for TGV operations between the capital and the major southeastern 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: Lille–Lyon
Triple: [TGV, route, Lille–Lyon]
Generated description
Lille–Lyon is a high-speed rail connection in France linking the northern city of Lille with Lyon, a major hub in the southeast.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lille–Lyon
Target entity description: Lille–Lyon is a high-speed rail connection in France linking the northern city of Lille with Lyon, a major hub in the southeast.
  • A. Paris–Lille
    Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
  • B. Paris–Montpellier
    Paris–Montpellier is a major high-speed rail corridor in France linking the capital with the Mediterranean city of Montpellier.
  • C. Paris–Strasbourg
    Paris–Strasbourg is a major high-speed rail corridor in France linking the capital with the Alsatian city near the German border.
  • D. Paris–Bayonne
    Paris–Bayonne is a major French rail corridor linking the capital Paris with the southwestern city of Bayonne, serving as a key route toward the Atlantic coast and the Spanish border.
  • E. Paris–Lyon
    Paris–Lyon is the pioneering high-speed rail corridor in France that became the flagship route for TGV operations between the capital and the major southeastern city.
  • 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_69d8b9f1a6d881909f024bc603111cdb completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48d2b2ea08190926ec0cf01285833 completed April 19, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a03211f669c8190881f8f9c50ce87c2 completed May 12, 2026, 12:46 p.m.
NEDg Description generation batch_6a03220af8dc8190aec1469d5c3d63be completed May 12, 2026, 12:50 p.m.
NED2 Entity disambiguation (via description) batch_6a032277e9c8819084a9d4c6f559bebc completed May 12, 2026, 12:52 p.m.
Created at: April 10, 2026, 10:16 a.m.