Triple

T3039253
Position Surface form Disambiguated ID Type / Status
Subject Sigismund II Augustus E83084 entity
Predicate residence P75 FINISHED
Object Vilnius 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 | Statement: [Sigismund II Augustus, residence, Vilnius]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilnius
Context triple: [Sigismund II Augustus, residence, Vilnius]
  • A. 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.
  • B. Kaunas
    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. Vilna
    Vilna is the historical name for Vilnius, the capital city of Lithuania and a major cultural and political center of the 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_69ad8b2298908190a7cb4e9bdbf064d0 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9b2fa52c8190a7860f762d5232ab completed March 8, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f86cfcac81908122f1afd79ce29a completed March 11, 2026, 11:19 p.m.
Created at: March 8, 2026, 3:01 p.m.