Triple

T9949552
Position Surface form Disambiguated ID Type / Status
Subject Southern Great Plain region E195293 entity
Predicate containsCity P294 FINISHED
Object Békéscsaba E446284 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: Békéscsaba | Statement: [Southern Great Plain region, containsCity, Békéscsaba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Békéscsaba
Context triple: [Southern Great Plain region, containsCity, 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. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • D. 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.
  • E. Kalocsa
    Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
  • 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_69ca82e96a108190932bd1fc4acd73a0 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb65a4e6c8190968192a24aad1b7d completed April 2, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69e5424a26688190a49c3920d0edb546 completed April 19, 2026, 8:59 p.m.
Created at: March 30, 2026, 8:45 p.m.