Triple

T6454338
Position Surface form Disambiguated ID Type / Status
Subject Lithuanian Jewry E139954 entity
Predicate associatedWithCity P1481 FINISHED
Object Kaunas 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 | Statement: [Lithuanian Jewry, associatedWithCity, Kaunas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaunas
Context triple: [Lithuanian Jewry, associatedWithCity, Kaunas]
  • 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. 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.
  • C. Vilnius
    Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
  • 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_69c008b301948190a35854e5284dc822 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069d339788190992e3299ffe30d58 completed March 22, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c65394d8b481909868faf79f3d2383 completed March 27, 2026, 9:53 a.m.
Created at: March 22, 2026, 4:48 p.m.