Triple

T19629616
Position Surface form Disambiguated ID Type / Status
Subject U-Bahn station Walther-Schreiber-Platz E471230 entity
Predicate hasAdjacentStation P231 FINISHED
Object Schloßstraße 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: Schloßstraße | Statement: [U-Bahn station Walther-Schreiber-Platz, hasAdjacentStation, Schloßstraße]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Schloßstraße
Context triple: [U-Bahn station Walther-Schreiber-Platz, hasAdjacentStation, Schloßstraße]
  • A. Schloßstraße chosen
    Schloßstraße is a Berlin U-Bahn station on the U9 line located in the Steglitz district, serving as a key access point to the nearby Schlossstraße shopping area.
  • B. Elbestraße
    Elbestraße is a street in Frankfurt am Main’s central Bahnhofsviertel district, known for its nightlife, diverse culture, and proximity to the main train station.
  • C. Burgenstraße
    Burgenstraße is a famous German tourist route known for connecting numerous historic castles and picturesque medieval towns.
  • D. Burgstraße
    Burgstraße is a historic street located in the Old Town (Altstadt) of Hanover, Germany, known for its traditional architecture and central location.
  • E. Kaufingerstraße
    Kaufingerstraße is one of Munich’s main and oldest pedestrian shopping streets, lined with stores and historic buildings in the city center.
  • 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_69d8e511f28481909f4bc3ea9191e54a completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e64101a0448190ba19f8917ae85dd6 completed April 20, 2026, 3:06 p.m.
Created at: April 10, 2026, 1:44 p.m.