Triple

T22495531
Position Surface form Disambiguated ID Type / Status
Subject Roman Catholic Diocese of Győr E556128 entity
Predicate cathedralCity P9022 FINISHED
Object Győr NE NERFINISHED

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: Győr | Statement: [Roman Catholic Diocese of Győr, cathedralCity, Győr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Győr
Context triple: [Roman Catholic Diocese of Győr, cathedralCity, Győr]
  • A. Győr chosen
    Győr is a historic city in northwestern Hungary, known as an important regional cultural and economic center at the confluence of the Danube, Rába, and Rábca rivers.
  • B. Diósgyőr
    Diósgyőr is a historic district of Miskolc in northeastern Hungary, best known for its medieval castle and surrounding cultural heritage.
  • C. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • D. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • E. Gödöllő
    Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e5445bc8190b6a9481926db3355 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15cb19bac81909bbc2f131186aeea completed April 29, 2026, 1:19 a.m.
Created at: April 16, 2026, 8:49 p.m.