Triple

T267848
Position Surface form Disambiguated ID Type / Status
Subject Metropolregion Mitteldeutschland E5771 entity
Predicate hasMember P10 FINISHED
Object City of 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: City of Chemnitz | Statement: [Metropolregion Mitteldeutschland, hasMember, City of Chemnitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Chemnitz
Context triple: [Metropolregion Mitteldeutschland, hasMember, City of Chemnitz]
  • A. Chemnitz chosen
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • B. 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.
  • C. 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.
  • D. Leipzig
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • E. Görlitz
    Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
  • 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_69a2587daeb081909591b9d30f80a271 completed Feb. 28, 2026, 2:52 a.m.
NER Named-entity recognition batch_69a25dae4a0c8190a66cf6ed3889851c completed Feb. 28, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69a7bffe8a488190939b1a778db4f517 completed March 4, 2026, 5:15 a.m.
Created at: Feb. 28, 2026, 2:56 a.m.