Triple

T4144353
Position Surface form Disambiguated ID Type / Status
Subject Central Lithuania E89346 entity
Predicate hasUrbanCenter P2106 FINISHED
Object Kaunas metropolitan area E14945 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: Kaunas metropolitan area | Statement: [Central Lithuania, hasUrbanCenter, Kaunas metropolitan area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaunas metropolitan area
Context triple: [Central Lithuania, hasUrbanCenter, Kaunas metropolitan area]
  • A. Kaunas District Municipality
    Kaunas District Municipality is an administrative region in central Lithuania that surrounds the city of Kaunas and includes numerous suburban and rural communities.
  • B. Kaunas chosen
    Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
  • C. Klaipėda
    Klaipėda is a Lithuanian port city on the Baltic Sea known as the country’s main maritime gateway and a key regional transport and industrial hub.
  • D. Panevėžys
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • E. Vilkaviškis
    Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
  • 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_69aed95785788190ae75bcf0cd1cafdf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af025d2984819095f299327cc399d5 completed March 9, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd7f60b49481908b2199544868769c completed March 20, 2026, 5:09 p.m.
Created at: March 9, 2026, 3:43 p.m.