Triple

T15345283
Position Surface form Disambiguated ID Type / Status
Subject Norheimsund E366899 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Tørvikbygd E1147722 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: Tørvikbygd | Statement: [Norheimsund, hasNearbySettlement, Tørvikbygd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tørvikbygd
Context triple: [Norheimsund, hasNearbySettlement, Tørvikbygd]
  • A. Tørvikbygd chosen
    Tørvikbygd is a small coastal village in the municipality of Kvam in Vestland county, western Norway, known for its scenic fjordside setting and traditional rural character.
  • B. Vestbygd
    Vestbygd is a small settlement in the municipality of Lødingen in Nordland county, Norway.
  • C. Teigebyen
    Teigebyen is a village in Viken county, Norway, serving as the main local hub for municipal services and community life in Nannestad.
  • D. Nordbygda
    Nordbygda is a small rural settlement located within Løten municipality in Innlandet county, Norway.
  • E. Husøy
    Husøy is a small fishing village located on an island off the coast of Senja in northern Norway, known for its dramatic coastal scenery and tightly clustered houses.
  • 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_69d85a1355608190a6673ddb67231d54 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e163a3c8190ab933411372c1573 completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01f931408190828d87567cecaceb completed May 9, 2026, 9:44 a.m.
Created at: April 10, 2026, 3:17 a.m.