Triple

T857890
Position Surface form Disambiguated ID Type / Status
Subject Christian Wolff E18533 entity
Predicate birthPlace P1 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: [Christian Wolff, birthPlace, Breslau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Breslau
Context triple: [Christian Wolff, birthPlace, Breslau]
  • A. Frankfurt (Oder)
    Frankfurt (Oder) is a German city on the Oder River at the Polish border, known as a historic university and trade center in the state of Brandenburg.
  • 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. 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.
  • D. 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.
  • E. 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.
  • 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_69a4938bdd3c8190a954a3c11844d9cf completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac4f740881909cb59a6c18a77af3 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69acddfa1d48819081538d6505fa062c completed March 8, 2026, 2:24 a.m.
Created at: March 1, 2026, 7:39 p.m.