Triple

T1418014
Position Surface form Disambiguated ID Type / Status
Subject Austrian Silesia E31962 entity
Predicate majorCity P316 FINISHED
Object Bielsko E90431 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: Bielsko | Statement: [Austrian Silesia, majorCity, Bielsko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bielsko
Context triple: [Austrian Silesia, majorCity, Bielsko]
  • A. Bielsko-Biała chosen
    Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
  • 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. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • D. Tarnobrzeg
    Tarnobrzeg is a city in southeastern Poland known historically for its sulfur mining industry and location along the Vistula River.
  • E. Ojców
    Ojców is a small village in southern Poland known as a gateway to the picturesque Ojców National Park in 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_69a49919a994819086528951bc224775 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c404e92c8190bd018673383f4534 completed March 1, 2026, 10:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69afc61b715c81908bb4849b03617d85 completed March 10, 2026, 7:19 a.m.
Created at: March 1, 2026, 7:59 p.m.