Triple

T8597486
Position Surface form Disambiguated ID Type / Status
Subject Frýdek E203586 entity
Predicate nearbyEntity P39585 FINISHED
Object Místek E746324 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: Místek | Statement: [Frýdek, nearbyEntity, Místek]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Místek
Context triple: [Frýdek, nearbyEntity, Místek]
  • A. Místek chosen
    Místek is a town in the Czech Republic that now forms part of the city of Frýdek-Místek following their administrative merger.
  • B. Mukalla
    Mukalla is a major port city on Yemen’s southern coast and the capital of the Hadhramaut Governorate.
  • C. Tivissa
    Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
  • D. Ostredok
    Ostredok is the highest peak of the Veľká Fatra range in central Slovakia, known for its rounded grassy summit and panoramic views.
  • E. Munirka
    Munirka is a densely populated residential and commercial neighborhood in South Delhi, known for its urban village character, proximity to major institutions, and extensive rental housing.
  • 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_69ca832b56948190ba751cec255308f1 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc46cacbe88190b95beeedc9f480b0 completed March 31, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebba872b8819098ba7525944bcce1 completed April 2, 2026, 6:55 p.m.
Created at: March 30, 2026, 6:24 p.m.