Triple

T1654712
Position Surface form Disambiguated ID Type / Status
Subject Museum Gunzenhauser E35771 entity
Predicate city P40 FINISHED
Object Chemnitz E1679 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: Chemnitz | Statement: [Museum Gunzenhauser, city, Chemnitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chemnitz
Context triple: [Museum Gunzenhauser, city, Chemnitz]
  • A. Chemnitz chosen
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • B. Zwickau
    Zwickau is a city in the German state of Saxony known historically as an important center of the automotive industry and as the birthplace of composer Robert Schumann.
  • C. Dresden
    Dresden is a small community within the municipality of Chatham-Kent in southwestern Ontario, Canada, known historically for its role in the Underground Railroad and Black settlement.
  • D. Dresden
    Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
  • E. Cottbus
    Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a8b597c81908a62b41718d85df6 completed March 5, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc189de08190b88849333d824c5b completed March 11, 2026, 5:22 a.m.
Created at: March 4, 2026, 7:29 p.m.