Triple

T12309537
Position Surface form Disambiguated ID Type / Status
Subject Šamorín E293439 entity
Predicate hasTwinTown P919 FINISHED
Object Mosonmagyaróvár E663578 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: Mosonmagyaróvár | Statement: [Šamorín, hasTwinTown, Mosonmagyaróvár]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mosonmagyaróvár
Context triple: [Šamorín, hasTwinTown, Mosonmagyaróvár]
  • A. Mosonmagyaróvár chosen
    Mosonmagyaróvár is a historic town in northwestern Hungary near the Austrian and Slovak borders, known for its thermal baths and strategic location as a regional transport hub.
  • B. Mátészalka
    Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
  • C. Csákvár
    Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
  • D. Balmazújváros
    Balmazújváros is a town in eastern Hungary known for its agricultural surroundings and location near the Hortobágy National Park.
  • E. Nagykőrös
    Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
  • 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_69d6ab6a2b50819082f6aedd32ed608a completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f01ace8819087f245b9216f4dc8 completed April 10, 2026, 6:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e8243d48190baf25b2927de6c62 completed May 2, 2026, 3:55 p.m.
Created at: April 8, 2026, 9:53 p.m.