Triple

T2828485
Position Surface form Disambiguated ID Type / Status
Subject Bruxelles-Midi / Brussel-Zuid E54979 entity
Predicate servedBy P82 FINISHED
Object Eurostar 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 | Statement: [Bruxelles-Midi / Brussel-Zuid, servedBy, Eurostar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurostar
Context triple: [Bruxelles-Midi / Brussel-Zuid, servedBy, Eurostar]
  • 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. 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.
  • D. Intercités
    Intercités is a network of French long-distance conventional trains operated by SNCF, connecting major cities and regions across the country.
  • E. TGV Ouigo
    TGV Ouigo is a low-cost high-speed train service operated by SNCF in France, offering budget fares on selected TGV routes.
  • 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_69ab49e100c0819082a40cb797383243 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde97168c8190b31122b2ad9fdebf completed March 7, 2026, 8:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69afceb4f3ec8190987afb7a2ac302e2 completed March 10, 2026, 7:56 a.m.
Created at: March 6, 2026, 9:59 p.m.