Triple

T17793785
Position Surface form Disambiguated ID Type / Status
Subject Karlskoga Municipality E444234 entity
Predicate hasTwinTown P919 FINISHED
Object Viljandi 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: Viljandi | Statement: [Karlskoga Municipality, hasTwinTown, Viljandi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Viljandi
Context triple: [Karlskoga Municipality, hasTwinTown, Viljandi]
  • A. Viljandi chosen
    Viljandi is a historic town in southern Estonia known for its medieval castle ruins, rich cultural life, and annual folk music festival.
  • B. Jõgeva
    Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
  • C. Võru
    Võru is a small town in southeastern Estonia known for its lakeside setting, traditional Võro culture, and role as a regional administrative and cultural center.
  • D. Rieste
    Rieste is a small municipality in Lower Saxony, Germany, situated within the Osnabrück district.
  • E. Kohtla-Järve
    Kohtla-Järve is an industrial city in northeastern Estonia known for its oil shale industry and diverse population.
  • 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_69d8b9efe370819095cd219b143ae727 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e487993a6c8190805e06d93dfc0dce completed April 19, 2026, 7:43 a.m.
Created at: April 10, 2026, 10:13 a.m.