Triple

T19603523
Position Surface form Disambiguated ID Type / Status
Subject Trnava E470542 entity
Predicate twinCity P1072 FINISHED
Object Békéscsaba NE NERFINISHED

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: Békéscsaba | Statement: [Trnava, twinCity, Békéscsaba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Békéscsaba
Context triple: [Trnava, twinCity, Békéscsaba]
  • A. Békéscsaba chosen
    Békéscsaba is a city in southeastern Hungary known as the administrative center of Békés County and for its cultural and culinary traditions, including its famous sausage.
  • B. Kecskemét
    Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
  • C. Szekesfehervar
    Szekesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • D. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • E. Szekszárd
    Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e510024481908415c0d616fa6186 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e64081af6c8190868b73b07c874cd5 completed April 20, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:43 p.m.