Triple

T18632446
Position Surface form Disambiguated ID Type / Status
Subject Lake Möckeln E455452 entity
Predicate borders P224 FINISHED
Object Karlskoga 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: Karlskoga | Statement: [Lake Möckeln, borders, Karlskoga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karlskoga
Context triple: [Lake Möckeln, borders, Karlskoga]
  • A. Karlskoga chosen
    Karlskoga is an industrial town in central Sweden known for its historical association with Alfred Nobel and its role in the country’s arms and engineering industries.
  • B. Karlshamn
    Karlshamn is a coastal town in southern Sweden known for its harbor, archipelago, and role as a regional industrial and transport hub.
  • C. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • D. Karlstad
    Karlstad is a city in central Sweden known as the capital of Värmland County, situated on the northern shore of Lake Vänern.
  • E. Trollhättan
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • 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_69d8d38cc7948190a55ea64e5638994e completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e54fc5c7ec8190ab0c64f009583f96 completed April 19, 2026, 9:57 p.m.
Created at: April 10, 2026, 11:46 a.m.