Triple

T7741553
Position Surface form Disambiguated ID Type / Status
Subject Silesian University of Technology E175521 entity
Predicate hasCampusIn P4623 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 University of Technology, hasCampusIn, Zabrze]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zabrze
Context triple: [Silesian University of Technology, hasCampusIn, 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. Kluczbork
    Kluczbork is a town in southern Poland known as a local administrative, cultural, and economic center in the Opole region.
  • C. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • D. Zbrzyca
    Zbrzyca is a river in northern Poland that flows through the Pomeranian region before joining the Brda River.
  • E. Zawiercie
    Zawiercie is a town in southern Poland’s Silesian Voivodeship, known historically as an industrial and railway hub near the Kraków-Częstochowa Upland.
  • 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_69c6995f9c60819092e386192bd63c6f completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7035df9348190ad3f3d845207bf4d completed March 27, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69e343941ae481909489cf7a4abdba68 completed April 18, 2026, 8:40 a.m.
Created at: March 27, 2026, 4:07 p.m.