Triple

T5701990
Position Surface form Disambiguated ID Type / Status
Subject Nightjet E125684 entity
Predicate hasRoute P4374 FINISHED
Object Innsbruck–Amsterdam
Innsbruck–Amsterdam is an international overnight train route connecting the Austrian city of Innsbruck with the Dutch capital Amsterdam.
 E540893 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: Innsbruck–Amsterdam | Statement: [Nightjet, hasRoute, Innsbruck–Amsterdam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Innsbruck–Amsterdam
Context triple: [Nightjet, hasRoute, Innsbruck–Amsterdam]
  • A. Paris–Basel
    Paris–Basel is an international air route linking the French capital Paris with the Swiss city of Basel.
  • B. Paris–Vienna
    Paris–Vienna is the classic international rail corridor linking the French and Austrian capitals, historically served by luxury trains such as the Orient Express.
  • C. Paris–Amsterdam
    Paris–Amsterdam is a major international high-speed rail route linking the capitals of France and the Netherlands.
  • D. Brussels–Cologne
    Brussels–Cologne is a major international high-speed rail corridor linking Belgium’s capital with the German city of Cologne.
  • E. Paris–Budapest
    Paris–Budapest is a historic international rail connection linking the French and Hungarian capitals, notably served by the famed Orient Express.
  • 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: Innsbruck–Amsterdam
Triple: [Nightjet, hasRoute, Innsbruck–Amsterdam]
Generated description
Innsbruck–Amsterdam is an international overnight train route connecting the Austrian city of Innsbruck with the Dutch capital Amsterdam.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Innsbruck–Amsterdam
Target entity description: Innsbruck–Amsterdam is an international overnight train route connecting the Austrian city of Innsbruck with the Dutch capital Amsterdam.
  • A. Paris–Basel
    Paris–Basel is an international air route linking the French capital Paris with the Swiss city of Basel.
  • B. Paris–Vienna
    Paris–Vienna is the classic international rail corridor linking the French and Austrian capitals, historically served by luxury trains such as the Orient Express.
  • C. Paris–Amsterdam
    Paris–Amsterdam is a major international high-speed rail route linking the capitals of France and the Netherlands.
  • D. Brussels–Cologne
    Brussels–Cologne is a major international high-speed rail corridor linking Belgium’s capital with the German city of Cologne.
  • E. Paris–Budapest
    Paris–Budapest is a historic international rail connection linking the French and Hungarian capitals, notably served by the famed Orient Express.
  • 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_69c0082c96988190b3a6a201edce472a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0245581988190a819b8137533ed31 completed March 22, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a5fe4fc8190944a63a29da0fe3c completed March 22, 2026, 9:08 p.m.
NEDg Description generation batch_69c05c1d98b4819080ae9163a0cfd659 completed March 22, 2026, 9:16 p.m.
NED2 Entity disambiguation (via description) batch_69c05caea8b881908a4d12aec44f422e completed March 22, 2026, 9:18 p.m.
Created at: March 22, 2026, 3:45 p.m.