Triple

T14902444
Position Surface form Disambiguated ID Type / Status
Subject Budapest Cog-wheel Railway E360038 entity
Predicate owner P347 FINISHED
Object City of Budapest E13406 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: City of Budapest | Statement: [Budapest Cog-wheel Railway, owner, City of Budapest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Budapest
Context triple: [Budapest Cog-wheel Railway, owner, City of 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. Donau City
    Donau City is a modern business and residential district in Vienna known for its high-rise buildings and proximity to the Danube River.
  • 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. Inner City of Pest
    The Inner City of Pest is the historic central district of Budapest, Hungary, known for its dense urban fabric, commercial streets, and many of the city’s key civic and cultural landmarks.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded60b24008190bd272c0d61329400 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a5f38488190b441dd0b385024b1 completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 2:11 a.m.