Triple

T6335988
Position Surface form Disambiguated ID Type / Status
Subject Berlin Hauptbahnhof E142491 entity
Predicate serves P98 FINISHED
Object EuroCity E132580 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: EuroCity | Statement: [Berlin Hauptbahnhof, serves, EuroCity]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: EuroCity
Context triple: [Berlin Hauptbahnhof, serves, EuroCity]
  • A. EuroCity trains chosen
    EuroCity trains are a network of high-quality international express passenger services that connect major cities across European countries with fast, comfortable, and cross-border rail travel.
  • B. InterCityExpress
    InterCityExpress is Germany’s high-speed train service operated by Deutsche Bahn, known for fast long-distance connections between major cities and neighboring countries.
  • C. Thalys
    Thalys is a high-speed international train service connecting major cities in France, Belgium, the Netherlands, and Germany.
  • 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. Intercity Express Train
    The Intercity Express Train is a modern high-speed passenger train used on long-distance routes in the UK, known for faster journeys, improved comfort, and greater energy efficiency compared to older rolling stock.
  • 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_69c008d4d8e88190ad301c05b08722ac completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0654a88a881908d5cb2aa7f22c4c7 completed March 22, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6042ab22c8190a7486049f45a546b completed March 27, 2026, 4:14 a.m.
Created at: March 22, 2026, 4:30 p.m.