Triple

T3498507
Position Surface form Disambiguated ID Type / Status
Subject Französische Straße E73907 entity
Predicate near P350 FINISHED
Object Friedrichstraße E73425 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: Friedrichstraße | Statement: [Französische Straße, near, Friedrichstraße]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Friedrichstraße
Context triple: [Französische Straße, near, Friedrichstraße]
  • A. Friedrichstraße chosen
    Friedrichstraße is a major central Berlin transport hub and historic thoroughfare known for its shopping, cultural venues, and role as a former border crossing during the Cold War.
  • B. Chausseestraße
    Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
  • C. Leipziger Straße
    Leipziger Straße is a major historic thoroughfare in central Berlin, known for its government buildings, commercial centers, and role in the city’s urban core.
  • D. Gerichtstraße
    Gerichtstraße is a street in Berlin, Germany, located in the Wedding district and known for its mix of residential buildings, commercial spaces, and cultural venues.
  • E. Yorckstraße
    Yorckstraße is a major street and transport corridor in Berlin’s Kreuzberg and Schöneberg districts, known for its multiple S-Bahn stations and proximity to several historic cemeteries and railway viaducts.
  • 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_69ad85cdb6e48190a335d412b9194ed8 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbbd3ad548190abbfae820bf3b66d completed March 8, 2026, 6:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b2bcadc819093b827a28e3e8930 completed March 14, 2026, 2:05 p.m.
Created at: March 8, 2026, 3:18 p.m.