Triple

T10182040
Position Surface form Disambiguated ID Type / Status
Subject Tolna County E236810 entity
Predicate capital P234 FINISHED
Object Szekszárd E587450 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: Szekszárd | Statement: [Tolna County, capital, Szekszárd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Szekszárd
Context triple: [Tolna County, capital, Szekszárd]
  • A. Szekszárd chosen
    Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
  • B. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • C. Békéscsaba
    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.
  • D. Zalaegerszeg
    Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
  • E. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • 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_69ca84d7260c8190bfbec36762943f37 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded32b91c8190b01ad37b2456080a completed April 2, 2026, 4:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69f78ac420788190b12167aef7436c64 completed May 3, 2026, 5:49 p.m.
Created at: March 30, 2026, 9:12 p.m.