Triple

T3884656
Position Surface form Disambiguated ID Type / Status
Subject Hugo Steinhaus E92909 entity
Predicate workLocation P7 FINISHED
Object Wrocław 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: Wrocław | Statement: [Hugo Steinhaus, workLocation, Wrocław]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wrocław
Context triple: [Hugo Steinhaus, workLocation, Wrocław]
  • A. 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.
  • B. Katowice
    Katowice is a major industrial and cultural city in southern Poland, known as the capital of the Silesian region.
  • C. Poznań
    Poznań is a historic and economically significant city in western Poland, known for its medieval Old Town, role as an early center of Polish statehood, and status as a major academic and industrial hub.
  • D. Kraków
    Kraków is one of Poland’s oldest and most historically significant cities, renowned for its well-preserved medieval core, royal heritage, and cultural institutions.
  • E. Opole Silesia
    Opole Silesia is a historical and cultural region in southwestern Poland, known for its mixed Polish-German heritage and centered around the city of Opole.
  • 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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec92cc548190b88b899299e5ccdc completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfe1423aa481909fd062c54779a3a2 completed March 22, 2026, 12:32 p.m.
Created at: March 9, 2026, 3:20 p.m.