Triple

T10450583
Position Surface form Disambiguated ID Type / Status
Subject Rakkestad E246410 entity
Predicate hasAdministrativeCentre P1474 FINISHED
Object Rakkestad village E277705 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: Rakkestad village | Statement: [Rakkestad, hasAdministrativeCentre, Rakkestad village]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rakkestad village
Context triple: [Rakkestad, hasAdministrativeCentre, Rakkestad village]
  • A. Rakkestad chosen
    Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
  • B. Spikkestad
    Spikkestad is a village in Asker Municipality, Norway, serving as a terminus on a local commuter rail line and functioning largely as a residential suburb for the Oslo region.
  • C. Nannestad
    Nannestad is a rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to Oslo Airport Gardermoen.
  • D. Røyrvik
    Røyrvik is a small rural municipality in Trøndelag county, Norway, known for its mountainous landscapes, reindeer herding traditions, and proximity to Børgefjell National Park.
  • E. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fe0a6a548190a54212912f618e4e completed April 7, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8dc43e53081909e14cfe295d17cb2 completed April 10, 2026, 11:17 a.m.
Created at: April 6, 2026, 12:17 p.m.