Triple

T9914589
Position Surface form Disambiguated ID Type / Status
Subject Chernyakhovsk E185834 entity
Predicate hasRailConnectionTo P848 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: [Chernyakhovsk, hasRailConnectionTo, Vilnius]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilnius
Context triple: [Chernyakhovsk, hasRailConnectionTo, 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_69ca829b45f481909040f7b99a1976ed completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb53ba1ac8190ba655133b81596d7 completed April 2, 2026, 12:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69d20dd82edc8190b405a3969864af77 completed April 5, 2026, 7:23 a.m.
Created at: March 30, 2026, 8:41 p.m.