Triple

T15499180
Position Surface form Disambiguated ID Type / Status
Subject Széchenyi Hill E378903 entity
Predicate connectedTo P37 FINISHED
Object Városmajor E1126655 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: Városmajor | Statement: [Széchenyi Hill, connectedTo, Városmajor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Városmajor
Context triple: [Széchenyi Hill, connectedTo, Városmajor]
  • A. Városmajor chosen
    Városmajor is a park-filled neighborhood in Budapest known for its green spaces, recreational areas, and role as a local transport hub.
  • B. Várpalota
    Várpalota is a town in western Hungary known for its historical castle and industrial heritage.
  • C. Oroszvár
    Oroszvár is a historic locality in present-day western Slovakia (now part of Rusovce, a borough of Bratislava) known in part as a former residence of Princess Louise of Belgium.
  • D. Gyulafehérvár
    Gyulafehérvár, known today as Alba Iulia in Romania, is a historic city that served as the political and cultural center of Transylvania for centuries.
  • E. Nagyvázsony
    Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fb0aee081909db1c54349ec8492 completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a001f7d79348190aba1889a7eb3d7c8 completed May 10, 2026, 6:02 a.m.
Created at: April 10, 2026, 3:53 a.m.