Triple

T5313316
Position Surface form Disambiguated ID Type / Status
Subject InterCityExpress E119084 entity
Predicate notableRoute P22 FINISHED
Object Frankfurt–Paris
Frankfurt–Paris is a major international high-speed rail connection linking Germany and France, commonly served by InterCityExpress (ICE) trains.
 E510348 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: Frankfurt–Paris | Statement: [InterCityExpress, notableRoute, Frankfurt–Paris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Frankfurt–Paris
Context triple: [InterCityExpress, notableRoute, Frankfurt–Paris]
  • A. Paris–Brussels
    Paris–Brussels is a major international high-speed rail corridor linking the capitals of France and Belgium.
  • B. Paris–Strasbourg
    Paris–Strasbourg is a major high-speed rail corridor in France linking the capital with the Alsatian city near the German border.
  • 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. Frankfurt am Main
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • 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: Frankfurt–Paris
Triple: [InterCityExpress, notableRoute, Frankfurt–Paris]
Generated description
Frankfurt–Paris is a major international high-speed rail connection linking Germany and France, commonly served by InterCityExpress (ICE) trains.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Frankfurt–Paris
Target entity description: Frankfurt–Paris is a major international high-speed rail connection linking Germany and France, commonly served by InterCityExpress (ICE) trains.
  • A. Paris–Brussels
    Paris–Brussels is a major international high-speed rail corridor linking the capitals of France and Belgium.
  • B. Paris–Strasbourg
    Paris–Strasbourg is a major high-speed rail corridor in France linking the capital with the Alsatian city near the German border.
  • 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. Frankfurt am Main
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • 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_69bd446b57bc8190a513d2e6c40314f3 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd8536c06c81908ef8ba8c39b4fa30 completed March 20, 2026, 5:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf1106ef9c8190811f7b70e784c962 completed March 21, 2026, 9:43 p.m.
NEDg Description generation batch_69bf11a601c481908a8cb6ea2c04d6df completed March 21, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_69bf127799208190a47580ed7b9ad550 completed March 21, 2026, 9:49 p.m.
Created at: March 20, 2026, 1:54 p.m.