Triple

T17013961
Position Surface form Disambiguated ID Type / Status
Subject Vas County E412769 entity
Predicate containsSettlement P847 FINISHED
Object Körmend E559598 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: Körmend | Statement: [Vas County, containsSettlement, Körmend]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Körmend
Context triple: [Vas County, containsSettlement, Körmend]
  • A. Körmend chosen
    Körmend is a small town in western Hungary, known for its historic castle and location near the Austrian border.
  • B. Körmöcbánya
    Körmöcbánya is a historic mining town in present-day Slovakia, renowned for its medieval gold and coin-minting traditions.
  • C. Nagykörút
    Nagykörút is a major semicircular thoroughfare in central Budapest, lined with historic buildings, shops, and public transport routes, and serving as one of the city’s key urban arteries.
  • D. Nagykőrös
    Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
  • E. Kiskőrös
    Kiskőrös is a small town in southern Hungary known as the birthplace of the national poet Sándor Petőfi and for its wine-producing region.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d47e64f081908f43870c7564d0ae completed April 18, 2026, 6:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a011b4990948190861ff81f8fc3e8f2 completed May 10, 2026, 11:56 p.m.
Created at: April 10, 2026, 5:33 a.m.