Triple

T793370
Position Surface form Disambiguated ID Type / Status
Subject Gustav Kirchhoff E16963 entity
Predicate workLocation P7 FINISHED
Object Breslau E17157 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: Breslau | Statement: [Gustav Kirchhoff, workLocation, Breslau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Breslau
Context triple: [Gustav Kirchhoff, workLocation, Breslau]
  • A. 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.
  • B. 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.
  • C. Cieszyn Silesia
    Cieszyn Silesia is a historical and ethnically diverse borderland region centered around the city of Cieszyn, spanning areas of present-day Poland and the Czech Republic.
  • D. Wrocław chosen
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • E. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • 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_69a4936cb7448190914f5fe4b8d81607 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a79a3bbc81908d818c50a366b0f8 completed March 1, 2026, 8:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5e92d6b0819091fad60317eee455 completed March 7, 2026, 5:21 p.m.
Created at: March 1, 2026, 7:38 p.m.