Triple

T18114099
Position Surface form Disambiguated ID Type / Status
Subject MS Fæmund II E433556 entity
Predicate hasHomePort P3150 FINISHED
Object Synnervika 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: Synnervika | Statement: [MS Fæmund II, hasHomePort, Synnervika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Synnervika
Context triple: [MS Fæmund II, hasHomePort, Synnervika]
  • A. Synnervika chosen
    Synnervika is a small lakeside locality in Norway that serves as a key access point and harbor area on the shores of Lake Femunden.
  • B. Fresvik
    Fresvik is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and fruit farming.
  • C. Sennesvik
    Sennesvik is a small coastal village located on the island of Vestvågøy in Norway’s Lofoten archipelago.
  • D. Sykkylven
    Sykkylven is a municipality in Møre og Romsdal county, Norway, known for its fjord landscape and strong furniture manufacturing industry.
  • E. Holvik
    Holvik is a small coastal village in the former municipality of Vågsøy in western Norway, known for its scenic fjordside setting and proximity to the town of Måløy.
  • 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_69d8b90916008190a1f110bd7ced5473 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ddd4c7888190b85c39decdb0333f completed April 19, 2026, 1:51 p.m.
Created at: April 10, 2026, 10:28 a.m.