Triple

T6625735
Position Surface form Disambiguated ID Type / Status
Subject Białystok railway station E149794 entity
Predicate connectsTo P845 FINISHED
Object Suwałki E443345 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: Suwałki | Statement: [Białystok railway station, connectsTo, Suwałki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suwałki
Context triple: [Białystok railway station, connectsTo, Suwałki]
  • A. Suwałki chosen
    Suwałki is a city in northeastern Poland known for its cold climate, proximity to the Lithuanian border, and location within the historical region of Podlasie.
  • B. Siedlce
    Siedlce is a city in eastern Poland known as a local economic, cultural, and transportation hub.
  • C. Świnoujście
    Świnoujście is a Polish port city and seaside resort on the Baltic Sea, known for its wide beaches, spa facilities, and strategic location at the mouth of the Świna River.
  • D. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • E. Wałcz
    Wałcz is a town in northwestern Poland known for its lakes, forests, and role as a local administrative 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_69c687ee50048190aa151765bef16193 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af8187d881908b7a86f2cae5de23 completed March 27, 2026, 4:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69d4e916805881908496459dad525b8c completed April 7, 2026, 11:23 a.m.
Created at: March 27, 2026, 1:58 p.m.