Triple

T20886813
Position Surface form Disambiguated ID Type / Status
Subject Mattsies E514302 entity
Predicate locatedNear P294 FINISHED
Object Mindelheim 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: Mindelheim | Statement: [Mattsies, locatedNear, Mindelheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mindelheim
Context triple: [Mattsies, locatedNear, Mindelheim]
  • A. Mindelheim chosen
    Mindelheim is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former status as a princely seat.
  • B. Meisenthal
    Meisenthal is a village in northeastern France renowned for its historic glassmaking tradition and cultural heritage.
  • C. Meisdorf
    Meisdorf is a village in the Harz district of Saxony-Anhalt, Germany, known for its scenic location in the Selke Valley and the nearby Meisdorf Castle.
  • D. Dornstadt
    Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
  • E. Planegg
    Planegg is a municipality in the district of Munich in Bavaria, Germany, known for its scenic location along the Würm River and its proximity to the city of Munich.
  • 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_69e0b4f733f081908a401c0b7beb0b9f completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6d058d4dc81908398f8c75e30dc77 completed April 21, 2026, 1:18 a.m.
Created at: April 16, 2026, 12:46 p.m.