Triple

T6613131
Position Surface form Disambiguated ID Type / Status
Subject Silesian Voivodeship E149284 entity
Predicate containsCity P294 FINISHED
Object Zabrze E526141 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: Zabrze | Statement: [Silesian Voivodeship, containsCity, Zabrze]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zabrze
Context triple: [Silesian Voivodeship, containsCity, Zabrze]
  • A. Zabrze chosen
    Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
  • B. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • C. Zawiercie
    Zawiercie is a town in southern Poland’s Silesian Voivodeship, known historically as an industrial and railway hub near the Kraków-Częstochowa Upland.
  • D. Zgierz
    Zgierz is a city in central Poland, historically part of the industrial Łódź region and notable for its textile industry and role in regional trade.
  • E. Tychy
    Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial 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_69c687ebc680819094caf71faba2efe2 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af3890408190a0edf2f813b93196 completed March 27, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d2571ac8ec8190bf59c2cec5e2e6e2 completed April 5, 2026, 12:35 p.m.
Created at: March 27, 2026, 1:57 p.m.