Triple

T4446403
Position Surface form Disambiguated ID Type / Status
Subject Glomma E96298 entity
Predicate hasWaterfall P13549 FINISHED
Object Sarpsfossen E245873 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: Sarpsfossen | Statement: [Glomma, hasWaterfall, Sarpsfossen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarpsfossen
Context triple: [Glomma, hasWaterfall, Sarpsfossen]
  • A. Sarpsfossen waterfall chosen
    Sarpsfossen waterfall is one of Europe's most powerful waterfalls, historically important for Norway's timber and hydropower industries and a notable natural attraction in the Sarpsborg area.
  • B. Hellefoss
    Hellefoss is a hydroelectric dam and waterfall installation on the Drammenselva river in Norway.
  • C. Sognsvann
    Sognsvann is a popular recreational lake and surrounding forested area in northern Oslo, Norway, known for hiking, swimming, and outdoor activities.
  • D. Døvikfoss power station
    Døvikfoss power station is a hydroelectric power plant located on the Drammenselva river in Norway, generating renewable electricity from the river’s waterfall.
  • E. Bøylefoss
    Bøylefoss is a small settlement in the municipality of Froland in southern Norway, known historically for its waterfall and associated hydroelectric power development.
  • 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_69b345415ba481908df738e7174448ba completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355d1eba08190899d0a3c1684ce4e completed March 13, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69b613850eb88190b689a632b0e2b374 completed March 15, 2026, 2:03 a.m.
Created at: March 12, 2026, 11:32 p.m.