Triple

T20367705
Position Surface form Disambiguated ID Type / Status
Subject Kandó Kálmán Faculty of Electrical Engineering E496957 entity
Predicate city P40 FINISHED
Object Budapest 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: Budapest | Statement: [Kandó Kálmán Faculty of Electrical Engineering, city, Budapest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Budapest
Context triple: [Kandó Kálmán Faculty of Electrical Engineering, city, Budapest]
  • A. Budapest chosen
    Budapest is the capital and largest city of Hungary, renowned for its historic architecture, thermal baths, and prominent location along the Danube River.
  • B. Újbuda
    Újbuda is a major residential and commercial district on the Buda side of Budapest, known for its universities, cultural venues, and riverside areas along the Danube.
  • C. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • D. Budaörs
    Budaörs is a suburban town near Budapest in Hungary, known for its rapid post-communist development and role as a commercial and residential hub.
  • 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 (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_69e0b4a4f9b081908a5a021919c21ccb completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6787291d88190a526fe2461d2a7c6 completed April 20, 2026, 7:03 p.m.
Created at: April 16, 2026, 11:26 a.m.