Triple

T385767
Position Surface form Disambiguated ID Type / Status
Subject Romania E8777 entity
Predicate largestCity P235 FINISHED
Object Bucharest E31636 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: Bucharest | Statement: [Romania, largestCity, Bucharest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bucharest
Context triple: [Romania, largestCity, Bucharest]
  • A. Bucharest chosen
    Bucharest is the capital and largest city of Romania, known for its mix of historic architecture, wide boulevards, and its role as the country’s political, cultural, and economic center.
  • B. Constanța
    Constanța is a major Romanian coastal city and one of the largest and most important ports on the Black Sea.
  • C. Sofia
    Sofia is the capital and largest city of Bulgaria, known as a major cultural, economic, and historical center in the Balkans.
  • D. Budapest
    Budapest is the capital and largest city of Hungary, renowned for its historic architecture, thermal baths, and prominent location along the Danube River.
  • E. Ploiești, Romania
    Ploiești is a major Romanian city in Prahova County, historically known for its oil industry and refineries that made it a key European petroleum center.
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec4345a48190a413261cba4eafd7 completed Feb. 28, 2026, 1:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4239e5bcc8190918c5c90c77898c9 completed March 1, 2026, 11:31 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.