Triple

T10092632
Position Surface form Disambiguated ID Type / Status
Subject Borsod-Abaúj-Zemplén County E215779 entity
Predicate contains P35 FINISHED
Object Sátoraljaújhely E214382 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: Sátoraljaújhely | Statement: [Borsod-Abaúj-Zemplén County, contains, Sátoraljaújhely]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sátoraljaújhely
Context triple: [Borsod-Abaúj-Zemplén County, contains, Sátoraljaújhely]
  • A. Sátoraljaújhely chosen
    Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
  • B. 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.
  • C. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • D. Hajdúszoboszló
    Hajdúszoboszló is a Hungarian spa town renowned for its thermal baths and large water park, making it a major health and wellness tourism destination.
  • E. Zalaegerszeg
    Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
  • 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_69ca83a4947c8190823a7495dc5d96ed completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd05c3c0c8190927580717429a4e5 completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69f67c5a9dc881909b695f7e87dfcdf6 completed May 2, 2026, 10:36 p.m.
Created at: March 30, 2026, 9:01 p.m.