Triple

T661391
Position Surface form Disambiguated ID Type / Status
Subject TGV E11761 entity
Predicate notableRoute P22 FINISHED
Object Paris–Lille
Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
E87143 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–Lille | Statement: [TGV, notableRoute, Paris–Lille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris–Lille
Context triple: [TGV, notableRoute, Paris–Lille]
  • A. New York–Paris
    New York–Paris is a major transatlantic air route connecting the United States and France, linking New York City with the French capital.
  • B. Lille
    Lille is a historic industrial and cultural hub in northern France, known for its Flemish-influenced architecture, large student population, and role as a major European transport crossroads.
  • C. Le Mans
    Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
  • D. Reims
    Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
  • E. Paris–Roubaix
    Paris–Roubaix is one of cycling’s oldest and most prestigious one-day “Monument” races, famed for its brutal cobblestone sectors and often unpredictable, harsh conditions.
  • 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–Lille
Triple: [TGV, notableRoute, Paris–Lille]
Generated description
Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Paris–Lille
Target entity description: Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
  • A. New York–Paris
    New York–Paris is a major transatlantic air route connecting the United States and France, linking New York City with the French capital.
  • B. Lille
    Lille is a historic industrial and cultural hub in northern France, known for its Flemish-influenced architecture, large student population, and role as a major European transport crossroads.
  • C. Le Mans
    Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
  • D. Reims
    Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
  • E. Paris–Roubaix
    Paris–Roubaix is one of cycling’s oldest and most prestigious one-day “Monument” races, famed for its brutal cobblestone sectors and often unpredictable, harsh conditions.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fa954988190841740a587ace466 completed March 1, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69a63747e47481909877b49507b67c2c completed March 3, 2026, 1:20 a.m.
NEDg Description generation batch_69a645cff2a481908aa0b0cfde78c929 completed March 3, 2026, 2:22 a.m.
NED2 Entity disambiguation (via description) batch_69a64656026c8190834af887720f3a0a completed March 3, 2026, 2:24 a.m.
Created at: March 1, 2026, 7:36 p.m.