Triple

T21771685
Position Surface form Disambiguated ID Type / Status
Subject HK Acroni Jesenice E537447 entity
Predicate basedIn P40 FINISHED
Object Jesenice NE NERFINISHED

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: Jesenice | Statement: [HK Acroni Jesenice, basedIn, Jesenice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jesenice
Context triple: [HK Acroni Jesenice, basedIn, Jesenice]
  • A. Jesenice chosen
    Jesenice is an industrial town in northwestern Slovenia known historically for its steel industry and proximity to the Julian Alps.
  • B. Brežice
    Brežice is a historic town in eastern Slovenia known for its medieval castle, wine-growing region, and location near the confluence of the Sava and Krka rivers.
  • C. Slovenj Gradec
    Slovenj Gradec is a historic town in northern Slovenia known for its cultural heritage and picturesque Alpine surroundings.
  • D. Trebnje
    Trebnje is a small town in southeastern Slovenia known as a local cultural and administrative center in the historical region of Lower Carniola.
  • E. Kraslice
    Kraslice is a town in the Karlovy Vary Region of the Czech Republic, known historically for its musical instrument manufacturing.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c470759c819094a215757113562b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f031aea8c88190b4bc57df6f269ba6 completed April 28, 2026, 4:03 a.m.
Created at: April 16, 2026, 6:51 p.m.