Triple

T18456271
Position Surface form Disambiguated ID Type / Status
Subject Oulu sub-region E450909 entity
Predicate hasMunicipality P847 FINISHED
Object Muhos 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: Muhos | Statement: [Oulu sub-region, hasMunicipality, Muhos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muhos
Context triple: [Oulu sub-region, hasMunicipality, Muhos]
  • A. Muhos chosen
    Muhos is a municipality in Northern Ostrobothnia, Finland, known for its riverside landscapes along the Oulujoki and its proximity to the city of Oulu.
  • B. Nevinnomyssk
    Nevinnomyssk is an industrial city in southwestern Russia known for its chemical and energy production sectors.
  • C. Mirow
    Mirow is a small historic town in the Mecklenburg Lake District of northeastern Germany, known for its castle island and connections to the House of Mecklenburg-Strelitz.
  • D. Moravice
    Moravice is a river in the northern part of the historical Moravia region of the Czech Republic.
  • E. Boshof
    Boshof is a small town in South Africa’s Free State province, historically known for its agricultural economy and role in the Anglo-Boer War.
  • 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_69d8d38345688190b565eac2e4cd7935 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5264c10408190b2085ade88655c7d completed April 19, 2026, 7 p.m.
Created at: April 10, 2026, 11:31 a.m.