Triple

T13410115
Position Surface form Disambiguated ID Type / Status
Subject History of Lithuania E320063 entity
Predicate includesTopic P494 FINISHED
Object Vilnius question E105330 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: Vilnius question | Statement: [History of Lithuania, includesTopic, Vilnius question]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilnius question
Context triple: [History of Lithuania, includesTopic, Vilnius question]
  • A. Visaginas
    Visaginas is a town in northeastern Lithuania known for its Soviet-era origins and proximity to the now-decommissioned Ignalina Nuclear Power Plant.
  • B. Vilnius chosen
    Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
  • C. Rytas Vilnius
    Rytas Vilnius is a prominent professional basketball club from Vilnius, Lithuania, known for competing at the highest national and European levels.
  • D. Kupiškis
    Kupiškis is a small town in northeastern Lithuania known for its historical architecture and location within the ethnographic region of Aukštaitija.
  • E. Švenčionys
    Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
  • 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_69d806b943cc8190b6af624d385d7e12 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaeb3facc819088c1af3b59237e7a completed April 12, 2026, 2:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7307ccff08190aa4037aa5a48f7d0 completed May 3, 2026, 11:24 a.m.
Created at: April 9, 2026, 9:35 p.m.