Triple

T1011254
Position Surface form Disambiguated ID Type / Status
Subject Central Transdanubia E21827 entity
Predicate hasHistoricCity P3786 FINISHED
Object Veszprém E168418 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: Veszprém | Statement: [Central Transdanubia, hasHistoricCity, Veszprém]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Veszprém
Context triple: [Central Transdanubia, hasHistoricCity, Veszprém]
  • A. Veszprém chosen
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • B. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • C. Pécs
    Pécs is a historic cultural and university city in southwestern Hungary, renowned for its Roman and Ottoman heritage and its designation as a European Capital of Culture in 2010.
  • D. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • E. Székesfehérvár
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7a5651081909f16a5fadd3992a4 completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad293450488190ac8a795524ca0c2c completed March 8, 2026, 7:45 a.m.
Created at: March 1, 2026, 7:41 p.m.