Triple

T599672
Position Surface form Disambiguated ID Type / Status
Subject Saxony E11465 entity
Predicate contains P35 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: [Saxony, contains, Chemnitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chemnitz
Context triple: [Saxony, contains, 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 historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
  • 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_69a4932779b881908688590d59c71900 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49d78c0f08190b83ad89062ccb0b9 completed March 1, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad3072c3a881908c33159cdd55ae0b completed March 8, 2026, 8:16 a.m.
Created at: March 1, 2026, 7:35 p.m.