Triple

T13418025
Position Surface form Disambiguated ID Type / Status
Subject Austvågøy E313264 entity
Predicate hasTown P847 FINISHED
Object Henningsvær E325499 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: Henningsvær | Statement: [Austvågøy, hasTown, Henningsvær]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henningsvær
Context triple: [Austvågøy, hasTown, Henningsvær]
  • A. Henningsvær chosen
    Henningsvær is a picturesque fishing village in northern Norway, known for its traditional architecture, dramatic coastal scenery, and vibrant arts and tourism scene.
  • B. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • C. Skælskør
    Skælskør is a small coastal town in western Zealand, Denmark, known for its historic harbor, scenic fjord, and traditional Danish architecture.
  • D. Farsø
    Farsø is a small Danish town in North Jutland, best known as the birthplace of Nobel Prize–winning author Johannes V. Jensen.
  • E. Frederikshavn
    Frederikshavn is a port town in northern Jutland, Denmark, known for its ferry connections to Norway and Sweden and its maritime industry.
  • 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_69d806ad0c44819088833ae1ec9e9690 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaeb8416c8190a00dde0917c26f51 completed April 12, 2026, 2:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69f76ba16b948190b7e753368eabf012 completed May 3, 2026, 3:37 p.m.
Created at: April 9, 2026, 9:39 p.m.