Triple

T12877790
Position Surface form Disambiguated ID Type / Status
Subject Leipzig metropolitan region E308012 entity
Predicate containsCity P294 FINISHED
Object Böhlen E921039 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: Böhlen | Statement: [Leipzig metropolitan region, containsCity, Böhlen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Böhlen
Context triple: [Leipzig metropolitan region, containsCity, Böhlen]
  • A. Böhlen chosen
    Böhlen is a small town in the Leipzig district of Saxony, Germany, known for its lignite mining and power generation industries.
  • B. Borgholzhausen
    Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
  • C. Obergum
    Obergum is a small neighborhood or former village that is now part of the town of Winsum in the province of Groningen, the Netherlands.
  • D. Kulmbach
    Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
  • E. Trostberg
    Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970fa8474819086a8af3c90f3ca84 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7cb4d348190962ba5fa21fbb77b completed May 7, 2026, 8:36 p.m.
Created at: April 9, 2026, 5:38 p.m.