Triple

T7690127
Position Surface form Disambiguated ID Type / Status
Subject Raoul Wallenberg Square in Budapest E174222 entity
Predicate isInCapitalCityOf P5025 FINISHED
Object Hungary E5017 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: Hungary | Statement: [Raoul Wallenberg Square in Budapest, isInCapitalCityOf, Hungary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hungary
Context triple: [Raoul Wallenberg Square in Budapest, isInCapitalCityOf, Hungary]
  • A. Hungary chosen
    Hungary is a landlocked Central European country known for its rich history, distinct language (Hungarian), and capital city Budapest, famed for its thermal baths and architecture.
  • B. Austria and Hungary
    Austria and Hungary are neighboring Central European countries with closely linked histories, cultures, and transportation networks.
  • C. Ungar
    Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
  • D. Slovakia and Hungary
    Slovakia and Hungary are neighboring Central European countries that share a significant stretch of their border along the Danube River.
  • E. Havran
    Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
  • 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_69c6995966348190939e6c37ba272c06 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c702421dec8190a74f8ade992ca811 completed March 27, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8aca0b5b08190b178f0908612164c completed March 29, 2026, 4:37 a.m.
Created at: March 27, 2026, 4:02 p.m.