Triple

T22003182
Position Surface form Disambiguated ID Type / Status
Subject Augsburg Hauptbahnhof E543382 entity
Predicate hasNearby P350 FINISHED
Object Augsburg city centre 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: Augsburg city centre | Statement: [Augsburg Hauptbahnhof, hasNearby, Augsburg city centre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Augsburg city centre
Context triple: [Augsburg Hauptbahnhof, hasNearby, Augsburg city centre]
  • A. Augsburg Old Town chosen
    Augsburg Old Town is the historic city center of Augsburg, Germany, known for its well-preserved medieval streets, Renaissance architecture, and significant cultural landmarks.
  • B. Munich old town
    Munich old town is the historic city center of Munich, Germany, known for its medieval street layout, landmark churches, and major squares such as Marienplatz.
  • C. Rosenheim old town
    Rosenheim old town is the historic center of the Bavarian city of Rosenheim, known for its picturesque squares, traditional architecture, and lively pedestrian areas.
  • D. Augsburg
    Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
  • E. Kaufbeuren
    Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
  • 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_69e11e2c814c8190837d072789000486 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1276cab5c8190ac1236fde7e0394a completed April 28, 2026, 9:32 p.m.
Created at: April 16, 2026, 8:20 p.m.