Triple

T20314653
Position Surface form Disambiguated ID Type / Status
Subject ICE TD E510346 entity
Predicate usedOnService P2367 FINISHED
Object EuroCity NE NERFINISHED

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: [ICE TD, usedOnService, EuroCity]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: EuroCity
Context triple: [ICE TD, usedOnService, 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 (2 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_69e0b4c7491c8190961113c4283b10b0 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e67786f4dc8190b02a6c2a4338362d completed April 20, 2026, 6:59 p.m.
Created at: April 16, 2026, 11:19 a.m.