Triple

T10429326
Position Surface form Disambiguated ID Type / Status
Subject Skiptvet E245866 entity
Predicate administrativeCentre P1474 FINISHED
Object Meieribyen E866580 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: Meieribyen | Statement: [Skiptvet, administrativeCentre, Meieribyen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Meieribyen
Context triple: [Skiptvet, administrativeCentre, Meieribyen]
  • A. Meieribyen chosen
    Meieribyen is a village in Viken county, Norway, serving as the main local center of administration and services for the surrounding Skiptvet municipality.
  • B. Teigebyen
    Teigebyen is a village in Viken county, Norway, serving as the main local hub for municipal services and community life in Nannestad.
  • C. Bjerke
    Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
  • D. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • E. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea4b4b5881908ae23f8efeea482b completed April 7, 2026, 11:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d90d92510481909135a75b2f582795 completed April 10, 2026, 2:47 p.m.
Created at: April 6, 2026, 12:13 p.m.