Triple

T19251293
Position Surface form Disambiguated ID Type / Status
Subject Wagons-Lits restaurant cars E481398 entity
Predicate countryOfOperation P1083 FINISHED
Object Hungary 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: Hungary | Statement: [Wagons-Lits restaurant cars, countryOfOperation, Hungary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hungary
Context triple: [Wagons-Lits restaurant cars, countryOfOperation, Hungary]
  • A. Hungary chosen
    Hungary is a landlocked Central European country known for its rich history, distinct language (Hungarian), and capital city Budapest, famed for its thermal baths and architecture.
  • B. Ungarie
    Ungarie is a small rural town in New South Wales, Australia, known for its agricultural community and location within the Bland Shire local government area.
  • C. Austria and Hungary
    Austria and Hungary are neighboring Central European countries with closely linked histories, cultures, and transportation networks.
  • D. Ungar
    Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
  • E. Slovakia and Hungary
    Slovakia and Hungary are neighboring Central European countries that share a significant stretch of their border along the Danube River.
  • 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_69d8e8cd9d1081908a181d02b88b59b8 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fb30bf6c819094c44aceb544a023 completed April 20, 2026, 10:08 a.m.
Created at: April 10, 2026, 1:27 p.m.