Triple

T15751136
Position Surface form Disambiguated ID Type / Status
Subject Rana E381846 entity
Predicate hasFjord P56784 FINISHED
Object Ranfjorden 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: Ranfjorden | Statement: [Rana, hasFjord, Ranfjorden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ranfjorden
Context triple: [Rana, hasFjord, Ranfjorden]
  • A. Ranfjorden chosen
    Ranfjorden is a long, narrow fjord in Nordland county, Norway, known for its dramatic coastal landscape and the industrial town of Mo i Rana along its shores.
  • B. Randsfjorden
    Randsfjorden is one of Norway’s largest inland lakes, located in Eastern Norway and known for its elongated shape and surrounding forested landscapes.
  • C. Steinsfjorden
    Steinsfjorden is a lake in southeastern Norway, forming the northeastern arm of Tyrifjorden and known for its scenic surroundings and recreational activities.
  • D. Langfjorden
    Langfjorden is a notable fjord in Norway’s Møre og Romsdal county, characterized by its deep waters and steep surrounding mountains.
  • E. Vadheimsfjorden
    Vadheimsfjorden is a narrow Norwegian fjord known for its steep surrounding mountains and scenic coastal landscape.
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e05030e31081908c307a8dc7067db4 completed April 16, 2026, 2:57 a.m.
Created at: April 10, 2026, 4:47 a.m.