Triple

T6651775
Position Surface form Disambiguated ID Type / Status
Subject Polish–German border E150838 entity
Predicate hasBorderCity P15361 FINISHED
Object Świnoujście E433872 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: Świnoujście | Statement: [Polish–German border, hasBorderCity, Świnoujście]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Świnoujście
Context triple: [Polish–German border, hasBorderCity, Świnoujście]
  • A. Świnoujście chosen
    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.
  • B. Kwidzyn
    Kwidzyn is a historic town in northern Poland known for its medieval Teutonic castle complex and Gothic cathedral.
  • C. Olsztynek
    Olsztynek is a small historic town in northern Poland known for its open-air ethnographic museum and location within the picturesque Warmian-Masurian lake district.
  • D. Kościerzyna
    Kościerzyna is a historic town in northern Poland known as a local cultural and economic center within the Kashubian region.
  • E. Suwałki
    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.
  • 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_69c687f2c9508190a60b9aad31d3f358 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b0458fb48190a76d8d1d6273a92b completed March 27, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69d534fbc3fc8190b64611ef1de92865 completed April 7, 2026, 4:46 p.m.
Created at: March 27, 2026, 2:01 p.m.