Triple

T16693779
Position Surface form Disambiguated ID Type / Status
Subject Biała River E405659 entity
Predicate hasNameInLanguage P15 FINISHED
Object Biała E856069 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: Biała | Statement: [Biała River, hasNameInLanguage, Biała]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biała
Context triple: [Biała River, hasNameInLanguage, Biała]
  • A. Biała
    Biała is a former town in southern Poland that historically developed as a separate urban center before being merged with Bielsko to form the modern city of Bielsko-Biała.
  • B. Biała chosen
    Biała is a river in southern Poland known for flowing through the city of Bielsko-Biała before joining the Vistula basin.
  • C. Biała
    Biała is a small town in southwestern Poland known for its location within the Opole region and its traditional Silesian character.
  • D. Białołęka
    Białołęka is a rapidly developing residential district in the northeastern part of Warsaw, known for its modern housing estates and expanding infrastructure.
  • E. Biała Piska
    Biała Piska is a small town in northeastern Poland known for its location amid the lakes and forests of the Warmian-Masurian region.
  • 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_69d8838db21081909589220fd71440a4 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37eab93a081909aedc45f3f8f0e10 completed April 18, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00919acd308190a3f29040554b9cfc completed May 10, 2026, 2:09 p.m.
Created at: April 10, 2026, 5:19 a.m.