Triple

T3945723
Position Surface form Disambiguated ID Type / Status
Subject Kaunas Railway Station E92141 entity
Predicate locatedIn P40 FINISHED
Object Kaunas city centre 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 city centre | Statement: [Kaunas Railway Station, locatedIn, Kaunas city centre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaunas city centre
Context triple: [Kaunas Railway Station, locatedIn, Kaunas city centre]
  • A. 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.
  • B. Švenčionys
    Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
  • 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. Trakai
    Trakai is a historic Lithuanian town famed for its medieval island castle and former status as a political center of the Grand Duchy of Lithuania.
  • E. Vilnius
    Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
  • 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_69aed965502c8190904ebad1203a4ae8 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef0d8841081908d2c1de8e5758017 completed March 9, 2026, 4:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69b53ff967a88190b0100dbedb580e4d completed March 14, 2026, 11:01 a.m.
Created at: March 9, 2026, 3:24 p.m.