Triple

T17370891
Position Surface form Disambiguated ID Type / Status
Subject Hole E422310 entity
Predicate hasSettlement P1068 FINISHED
Object Steinsåsen NE ONNED1

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: Steinsåsen | Statement: [Hole, hasSettlement, Steinsåsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Steinsåsen
Context triple: [Hole, hasSettlement, Steinsåsen]
  • A. Steinsåsen chosen
    Steinsåsen is a small village in Hole Municipality in Viken county, Norway, situated near the Tyrifjorden lake and known for its scenic rural surroundings.
  • B. Utgårdskilen
    Utgårdskilen is a small coastal settlement and harbor area located in the Hvaler archipelago in southeastern Norway.
  • C. Stöllet
    Stöllet is a small locality in central Sweden situated within Torsby Municipality in Värmland County.
  • D. Kongens Have
    Kongens Have is a historic public park in central Copenhagen, Denmark, surrounding Rosenborg Castle and serving as one of the city's most popular green spaces.
  • E. Kongens Enghave
    Kongens Enghave is a district in Copenhagen, Denmark, known for its mix of industrial areas, harborfront developments, and residential neighborhoods.
  • 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_69d889d6535c81908be333c01deaec4e completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a68ff448190b505861e56df5b6d completed April 19, 2026, 2:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a019568a27c8190af1bbe6db75f3e6f in_progress May 11, 2026, 8:38 a.m.
Created at: April 10, 2026, 5:44 a.m.