Triple

T8518487
Position Surface form Disambiguated ID Type / Status
Subject Paris–Amsterdam E201635 entity
Predicate railNetwork P522 FINISHED
Object Eurostar network E39296 NE FINISHED

How this triple was built (2 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: Eurostar network | Statement: [Paris–Amsterdam, railNetwork, Eurostar network]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurostar network
Context triple: [Paris–Amsterdam, railNetwork, Eurostar network]
  • A. Eurostar chosen
    Eurostar is a high-speed international train service connecting the United Kingdom with mainland Europe via the Channel Tunnel, linking cities such as London, Paris, and Brussels.
  • B. Thalys
    Thalys is a high-speed international train service connecting major cities in France, Belgium, the Netherlands, and Germany.
  • C. European rail network
    The European rail network is an extensive, interconnected system of railway lines and services spanning multiple European countries, enabling both international and regional passenger and freight transport.
  • D. TGV Lyria
    TGV Lyria is a high-speed train service linking France and Switzerland, operated as a joint venture between SNCF and Swiss Federal Railways.
  • E. SNCF Connect
    SNCF Connect is the official digital platform and app of the French national railway company, providing online ticket booking, travel planning, and real-time information for trains and other transport services.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca8321bb44819081b74df0b710276d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe626787c819087e72dd76b2d9310 completed March 31, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf27fc84cc81909b788839bbc8e016 completed April 3, 2026, 2:37 a.m.
Created at: March 30, 2026, 6:16 p.m.