Triple

T9686007
Position Surface form Disambiguated ID Type / Status
Subject Great Reformed Church of Debrecen E234408 entity
Predicate isLandmarkOf P6629 FINISHED
Object Debrecen E37144 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: Debrecen | Statement: [Great Reformed Church of Debrecen, isLandmarkOf, Debrecen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Debrecen
Context triple: [Great Reformed Church of Debrecen, isLandmarkOf, Debrecen]
  • A. Debrecen chosen
    Debrecen is Hungary’s second-largest city and a key cultural, economic, and educational center in the country’s eastern region.
  • B. Szeged
    Szeged is a prominent city in southern Hungary known for its university, paprika production, and distinctive Art Nouveau architecture.
  • C. Békéscsaba
    Békéscsaba is a city in southeastern Hungary known as the administrative center of Békés County and for its cultural and culinary traditions, including its famous sausage.
  • D. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • E. Győr
    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.
  • 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_69ca84ca73208190957a900c8543bdcc completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9cd2dab481908e0d3fed28de9d40 completed April 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6f69ad0448190a2f472555384f0be completed April 9, 2026, 12:45 a.m.
Created at: March 30, 2026, 8:16 p.m.