Triple

T4099286
Position Surface form Disambiguated ID Type / Status
Subject Western Transdanubia E87898 entity
Predicate containsTouristRegion P32586 FINISHED
Object Sopron wine region E99815 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: Sopron wine region | Statement: [Western Transdanubia, containsTouristRegion, Sopron wine region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sopron wine region
Context triple: [Western Transdanubia, containsTouristRegion, Sopron wine region]
  • A. Tokaj
    Tokaj is a historic town in northeastern Hungary renowned worldwide for its Tokaji wine region and sweet dessert wines.
  • B. Neusiedler See wine region
    The Neusiedler See wine region is a renowned Austrian wine-growing area around Lake Neusiedl, famous for its sweet botrytized wines and high-quality white and red varieties.
  • C. Lechkhumi wine area
    Lechkhumi wine area is a historic Georgian wine-producing region in western Georgia, known for its mountainous vineyards and distinctive local grape varieties.
  • D. Sopron chosen
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • 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_69aed94564cc8190a9c1457daedb6e7f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefd0d9c508190b8aedf83f3310513 completed March 9, 2026, 5:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b7585bc81909dc2c02e60a55def completed March 14, 2026, 2:06 p.m.
Created at: March 9, 2026, 3:40 p.m.