Triple

T2892972
Position Surface form Disambiguated ID Type / Status
Subject Bytča E63869 entity
Predicate regionCapital P16248 FINISHED
Object Žilina E218081 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: Žilina | Statement: [Bytča, regionCapital, Žilina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Žilina
Context triple: [Bytča, regionCapital, Žilina]
  • A. Žilina chosen
    Žilina is a city in northwestern Slovakia that serves as an important industrial and transportation hub, particularly for rail connections in the region.
  • B. Trenčín
    Trenčín is a historic city in western Slovakia known for its medieval castle overlooking the Váh River and its role as a regional cultural and economic center.
  • C. Zlín
    Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
  • D. Kežmarok
    Kežmarok is a historic town in northern Slovakia known for its well-preserved medieval architecture and role as a cultural center of the Spiš (Spisz) region.
  • E. Banská Bystrica
    Banská Bystrica is a historic central Slovak city best known as the main center of the anti-Nazi Slovak National Uprising during World War II.
  • 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_69ab4c45822c8190830c5f2bb97bcfd0 completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe062234c81909411e34db7d2683d completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b324d0152881909526cf9079560dc6 completed March 12, 2026, 8:40 p.m.
Created at: March 6, 2026, 10:07 p.m.