Triple

T21265367
Position Surface form Disambiguated ID Type / Status
Subject Chico Buarque E524110 entity
Predicate notableWork P4 FINISHED
Object Budapeste 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: Budapeste | Statement: [Chico Buarque, notableWork, Budapeste]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Budapeste
Context triple: [Chico Buarque, notableWork, Budapeste]
  • 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. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • D. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • E. 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.
  • 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_69e0b5156d7881909bd4f83676590715 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e735ebe09081909f74301e91b4d3d7 completed April 21, 2026, 8:31 a.m.
Created at: April 16, 2026, 4 p.m.