Triple

T802500
Position Surface form Disambiguated ID Type / Status
Subject Wrocław E17157 entity
Predicate twinCity P1072 FINISHED
Object Hradec Králové E19071 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: Hradec Králové | Statement: [Wrocław, twinCity, Hradec Králové]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hradec Králové
Context triple: [Wrocław, twinCity, Hradec Králové]
  • A. Hradec Králové chosen
    Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
  • B. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • C. Ústí nad Labem
    Ústí nad Labem is an industrial city in the north of the Czech Republic, known as a major transport hub and river port in the Bohemian region.
  • D. Zlín
    Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
  • E. Brno
    Brno is the second-largest city in the Czech Republic, known as a major cultural, educational, and industrial center in the historical region of Moravia.
  • 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_69a49378b9c48190adbf5f62e5b7aca1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aabd9fc081908ccadd8e8769de2d completed March 1, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac660a86d881908ae96a5492c9b9a2 completed March 7, 2026, 5:53 p.m.
Created at: March 1, 2026, 7:38 p.m.