Triple

T13471737
Position Surface form Disambiguated ID Type / Status
Subject mainland Norway E311642 entity
Predicate hasLake P1025 FINISHED
Object Femunden E98766 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: Femunden | Statement: [mainland Norway, hasLake, Femunden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Femunden
Context triple: [mainland Norway, hasLake, Femunden]
  • A. Femunden chosen
    Femunden is one of Norway's largest lakes, located in the eastern part of the country near the Swedish border and known for its wilderness landscapes and outdoor recreation.
  • B. Eemnes
    Eemnes is a small town and municipality in the central Netherlands known for its characteristic polder landscape and historic village centers.
  • C. Nidelva
    Nidelva is the main river flowing through Trondheim, Norway, known for its scenic bends, historic waterfront buildings, and central role in the city’s landscape.
  • D. Nesset
    Nesset is a former municipality in western Norway known for its scenic fjord landscapes and rural communities.
  • E. Mossefjord
    Mossefjord is a coastal fjord in southeastern Norway known for bordering the town of Moss and forming part of the Oslofjord system.
  • 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_69d806a938b8819097ec43a2229fc7f9 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf22e5f88190b1078f006c8ef7c0 completed April 12, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7462d97688190b5b817fff5973ceb completed May 3, 2026, 12:57 p.m.
Created at: April 9, 2026, 9:42 p.m.