Triple

T11244829
Position Surface form Disambiguated ID Type / Status
Subject Krosno County E266174 entity
Predicate hasRuralGmina P46876 FINISHED
Object Chorkówka E626866 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: Chorkówka | Statement: [Krosno County, hasRuralGmina, Chorkówka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chorkówka
Context triple: [Krosno County, hasRuralGmina, Chorkówka]
  • A. Chorkówka chosen
    Chorkówka is a village in southeastern Poland known historically as the place where pioneering oil industry chemist and pharmacist Ignacy Łukasiewicz spent his final years and died.
  • B. Ciechocinek
    Ciechocinek is a Polish spa town renowned for its historic saline graduation towers and therapeutic health resorts.
  • C. Gubałówka
    Gubałówka is a popular hill and tourist destination in the Polish Tatra region, known for its panoramic views of Zakopane and the surrounding mountains.
  • D. Sokołówka
    Sokołówka is a small river in Poland known for flowing through the city of Łódź and its surrounding areas.
  • E. Chojnice
    Chojnice is a historic town in northern Poland known for its medieval architecture and role as a local cultural and economic 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_69d6aac7953c8190b82caf9d7640fdf9 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e91c045c81908a9024a8aee32f4d completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65e9136bc8190b35685376da7007e completed May 2, 2026, 8:29 p.m.
Created at: April 8, 2026, 9:31 p.m.