Triple

T16124981
Position Surface form Disambiguated ID Type / Status
Subject Aichach-Friedberg E391244 entity
Predicate near P350 FINISHED
Object Augsburg E127006 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: Augsburg | Statement: [Aichach-Friedberg, near, Augsburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Augsburg
Context triple: [Aichach-Friedberg, near, Augsburg]
  • A. Augsburg chosen
    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.
  • B. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • C. Landshut
    Landshut is a historic Bavarian city in southeastern Germany known for its well-preserved medieval architecture and the landmark Trausnitz Castle.
  • D. Kaufbeuren
    Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
  • E. Ulm
    Ulm is a historic city in the German state of Baden-Württemberg, best known for its towering Gothic cathedral and as the birthplace of physicist Albert Einstein.
  • 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_69e2020408a88190bf3dfc893d577c55 completed April 17, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69fffee4f84c81908b2c7e216ef159e0 completed May 10, 2026, 3:43 a.m.
Created at: April 10, 2026, 5 a.m.