Triple

T16899217
Position Surface form Disambiguated ID Type / Status
Subject Margit Kovács Ceramic Museum E424389 entity
Predicate ownedBy P347 FINISHED
Object Town of Szentendre E95946 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: Town of Szentendre | Statement: [Margit Kovács Ceramic Museum, ownedBy, Town of Szentendre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Town of Szentendre
Context triple: [Margit Kovács Ceramic Museum, ownedBy, Town of Szentendre]
  • A. Szentendre chosen
    Szentendre is a picturesque riverside town near Budapest in Hungary, known for its baroque architecture, art galleries, and vibrant cultural scene.
  • B. Devecser
    Devecser is a small town in western Hungary known for its location in Veszprém County and for being affected by the 2010 Ajka alumina plant red sludge disaster.
  • C. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • D. Tihany
    Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
  • E. Kispest
    Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
  • 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_69d889da3e8c8190a2b118f383f0beac completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3c8da7b0481909111358871875023 completed April 18, 2026, 6:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012ecafd908190b8a1513138a29303 completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:29 a.m.