Triple

T11969952
Position Surface form Disambiguated ID Type / Status
Subject Lubusz Voivodeship E284891 entity
Predicate capital P234 FINISHED
Object Zielona Góra E149641 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: Zielona Góra | Statement: [Lubusz Voivodeship, capital, Zielona Góra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zielona Góra
Context triple: [Lubusz Voivodeship, capital, Zielona Góra]
  • A. Zielona Góra chosen
    Zielona Góra is a city in western Poland known for its wine-making tradition and annual wine festival.
  • B. Jelenia Góra
    Jelenia Góra is a historic city in southwestern Poland, known for its picturesque setting in the Karkonosze Mountains and its well-preserved old town architecture.
  • C. Inowrocław
    Inowrocław is a historic spa and industrial city in north-central Poland, known for its saltworks and location in the Kuyavia region.
  • D. Mielec
    Mielec is a town in southeastern Poland known for its aviation industry and manufacturing sector.
  • E. Gorzów Wielkopolski
    Gorzów Wielkopolski is a city in western Poland, known as one of the two capitals of the Lubusz Voivodeship and 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_69d6ab2eaeb881909f7914758f859413 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9037bee54819085242a3ef3e286f9 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a006ec7a4748190822e66a756bc95b9 completed May 10, 2026, 11:40 a.m.
Created at: April 8, 2026, 9:46 p.m.