Triple

T15947617
Position Surface form Disambiguated ID Type / Status
Subject Lauingen E386724 entity
Predicate hasSubdivision P747 FINISHED
Object Kernstadt Lauingen E386724 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: Kernstadt Lauingen | Statement: [Lauingen, hasSubdivision, Kernstadt Lauingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kernstadt Lauingen
Context triple: [Lauingen, hasSubdivision, Kernstadt Lauingen]
  • A. Lauingen chosen
    Lauingen is a historic Bavarian town in southern Germany, best known as the birthplace of the medieval scholar and philosopher Albert the Great.
  • B. Lautlingen
    Lautlingen is a village in the Zollernalb district of Baden-Württemberg, Germany, now incorporated as a district of the town of Albstadt.
  • C. Wuhletal
    Wuhletal is a valley landscape in Berlin shaped by the course of the Wuhle river, featuring green spaces, walking paths, and recreational areas.
  • D. Maihingen
    Maihingen is a small rural municipality in the Donau-Ries district of Bavaria in southern Germany.
  • E. Schwabhausen
    Schwabhausen is a municipality in Bavaria, Germany, known for its rural character and location within the greater Munich metropolitan region.
  • 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_69d86da882448190a82ea962fe343b79 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d2fda8819085279d2a0f8a02ab completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb5c0c8a481908aa7a40bca15e38e completed May 9, 2026, 10:31 p.m.
Created at: April 10, 2026, 4:53 a.m.