Triple

T11795957
Position Surface form Disambiguated ID Type / Status
Subject Štúrovo E280505 entity
Predicate hasHungarianName P27628 FINISHED
Object Párkány E781945 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: Párkány | Statement: [Štúrovo, hasHungarianName, Párkány]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Párkány
Context triple: [Štúrovo, hasHungarianName, Párkány]
  • A. Párkány chosen
    Párkány is a town on the Danube River in southern Slovakia, near the Hungarian border, historically known as a strategic site of conflicts between the Habsburg and Ottoman empires.
  • B. Balvanyos
    Balvanyos is a Romanian mountain resort area known for its natural mineral springs, spa facilities, and scenic surroundings in the Eastern Carpathians.
  • C. Mátészalka
    Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
  • D. Bonyhád
    Bonyhád is a town in southern Hungary known as an important local center within Tolna County.
  • E. Kozármisleny
    Kozármisleny is a small town in southern Hungary, near Pécs, known for its growing residential character and local sports culture.
  • 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a5a1cda0819092d66a82fd882786 completed April 10, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69f78ac420788190b12167aef7436c64 completed May 3, 2026, 5:49 p.m.
Created at: April 8, 2026, 9:42 p.m.