Triple

T14848784
Position Surface form Disambiguated ID Type / Status
Subject University of Vilnius E349172 entity
Predicate locatedIn P40 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: [University of Vilnius, locatedIn, Vilnius]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilnius
Context triple: [University of Vilnius, locatedIn, 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. Zarasai
    Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
  • D. 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.
  • 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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded43eee188190bf24dc475b3abe28 completed April 14, 2026, 11:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe6502d3f081909ff6fa8722769e2e completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:53 a.m.