Triple

T16129489
Position Surface form Disambiguated ID Type / Status
Subject Louisa von Trapp E391355 entity
Predicate setting P1957 FINISHED
Object Salzburg, Austria E19756 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: Salzburg, Austria | Statement: [Louisa von Trapp, setting, Salzburg, Austria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Salzburg, Austria
Context triple: [Louisa von Trapp, setting, Salzburg, Austria]
  • A. Salzburg chosen
    Salzburg is a historic Austrian city on the Salzach River, renowned for its baroque architecture, Alpine setting, and as the birthplace of composer Wolfgang Amadeus Mozart.
  • B. Gmunden, Austria
    Gmunden, Austria is a picturesque lakeside town in Upper Austria known for its historic ceramics industry, scenic location on Lake Traunsee, and role as a former residence of exiled European royalty.
  • C. Lenzburg
    Lenzburg is a historic Swiss town in the canton of Aargau, known for its medieval hilltop castle and well-preserved old town.
  • D. Haag, Austria
    Haag, Austria is a small town in Lower Austria best known as the birthplace of influential Bauhaus designer and artist Herbert Bayer.
  • E. Innsbruck
    Innsbruck is a city in western Austria known for its Alpine setting and winter sports facilities, and it later successfully hosted the Winter Olympics in 1964 and 1976.
  • 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_69d87f1bb0988190b490d273dbf3fd03 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e202075860819088d27d921609a6ce completed April 17, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff2aff07c8190bf693f652e2a2808 completed May 10, 2026, 2:51 a.m.
Created at: April 10, 2026, 5:01 a.m.