Triple

T10093704
Position Surface form Disambiguated ID Type / Status
Subject MVK Zrt. E215808 entity
Predicate serviceArea P82 FINISHED
Object city of Miskolc E38152 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: city of Miskolc | Statement: [MVK Zrt., serviceArea, city of Miskolc]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Miskolc
Context triple: [MVK Zrt., serviceArea, city of Miskolc]
  • A. Miskolc chosen
    Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
  • 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. Sátoraljaújhely
    Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
  • D. Kaposvár, Hungary
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • E. Makó
    Makó is a town in southeastern Hungary, renowned for its onion production and thermal baths.
  • 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_69cdd05da3688190b7a1ac8a58e5488f completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69e3a8c95ca081908ceaa89eef87fbc9 completed April 18, 2026, 3:52 p.m.
Created at: March 30, 2026, 9:01 p.m.