Triple

T3145327
Position Surface form Disambiguated ID Type / Status
Subject Hamar municipality E65749 entity
Predicate locatedBy P2409 FINISHED
Object Lake Mjøsa E65750 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: Lake Mjøsa | Statement: [Hamar municipality, locatedBy, Lake Mjøsa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Mjøsa
Context triple: [Hamar municipality, locatedBy, Lake Mjøsa]
  • A. Mjøsa Lake chosen
    Mjøsa Lake is Norway’s largest lake, located in the southeastern part of the country and known for its scenic surroundings and historic towns along its shores.
  • B. Øymarksjøen
    Øymarksjøen is a lake in southeastern Norway known for its forested surroundings, recreational fishing, and role in the local waterway system near the Swedish border.
  • C. Randsfjorden lake
    Randsfjorden lake is one of Norway’s largest inland lakes, known for its elongated shape, scenic surroundings, and importance for local recreation and fishing.
  • D. Rødenessjøen
    Rødenessjøen is a lake in Norway known for its scenic natural surroundings and recreational opportunities such as fishing and boating.
  • E. Sognsvann
    Sognsvann is a popular recreational lake and surrounding forested area in northern Oslo, Norway, known for hiking, swimming, and outdoor activities.
  • 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_69ad8582f564819088c27e1f96153938 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada59797788190a8d71262888c5df0 completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24b42fd948190bc9bcf44692c84ca completed March 12, 2026, 5:12 a.m.
Created at: March 8, 2026, 3:05 p.m.