Triple

T4485345
Position Surface form Disambiguated ID Type / Status
Subject Franconian Alb E107221 entity
Predicate near P350 FINISHED
Object Regensburg E127596 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: Regensburg | Statement: [Franconian Alb, near, Regensburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Regensburg
Context triple: [Franconian Alb, near, Regensburg]
  • A. Regensburg chosen
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • B. 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.
  • C. Bamberg
    Bamberg is a historic city in northern Bavaria, Germany, renowned for its well-preserved medieval old town and status as a UNESCO World Heritage Site.
  • D. 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.
  • E. Landshut
    Landshut is a historic Bavarian city in southeastern Germany known for its well-preserved medieval architecture and the landmark Trausnitz Castle.
  • 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_69bd43f84f788190a1383579c4a595be completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd52a758f48190b6b59ca0d9207c2a completed March 20, 2026, 1:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c124d359ec819086420b7e00b8d630 completed March 23, 2026, 11:32 a.m.
Created at: March 20, 2026, 12:59 p.m.