Triple

T5482713
Position Surface form Disambiguated ID Type / Status
Subject Stanisław Witkiewicz E123503 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: [Stanisław Witkiewicz, residence, Vilnius]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilnius
Context triple: [Stanisław Witkiewicz, 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. Vilkaviškis
    Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
  • E. Panevėžys
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • 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_69bd4648883481909e9775d43300c5fa completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd924bac088190b7d08df91534b0bc completed March 20, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf48a4c67081909fb62ddcf0fb3047 completed March 22, 2026, 1:40 a.m.
Created at: March 20, 2026, 2:09 p.m.