Triple

T19557503
Position Surface form Disambiguated ID Type / Status
Subject MPWiK Wrocław E489353 entity
Predicate serviceArea P82 FINISHED
Object city of Wrocław NE NERFINISHED

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 Wrocław | Statement: [MPWiK Wrocław, serviceArea, city of Wrocław]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Wrocław
Context triple: [MPWiK Wrocław, serviceArea, city of 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. Wrocław urban area
    Wrocław urban area is the metropolitan region centered on the city of Wrocław in southwestern Poland, encompassing its surrounding suburbs and satellite districts.
  • D. Bydgoszcz
    Bydgoszcz is a major city in northern Poland known as an important economic, cultural, and academic center on the Brda and Vistula rivers.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63d34d1bc81908e5e10f069655866 completed April 20, 2026, 2:50 p.m.
Created at: April 10, 2026, 1:42 p.m.