Triple

T9433178
Position Surface form Disambiguated ID Type / Status
Subject Lualaba River E227434 entity
Predicate mouthLocation P417 FINISHED
Object Boyoma Falls E613183 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: Boyoma Falls | Statement: [Lualaba River, mouthLocation, Boyoma Falls]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Boyoma Falls
Context triple: [Lualaba River, mouthLocation, Boyoma Falls]
  • A. Boyoma Falls chosen
    Boyoma Falls is a series of powerful cataracts on the Lualaba River in the Democratic Republic of the Congo, known as one of the largest waterfall systems in Africa by volume.
  • B. Tanda Falls
    Tanda Falls is a scenic waterfall and popular natural getaway located near Mirzapur in Uttar Pradesh, India.
  • C. Diyaluma Falls
    Diyaluma Falls is one of Sri Lanka’s tallest and most scenic waterfalls, renowned for its dramatic cascades and natural rock pools that attract many visitors.
  • D. Gurara Falls
    Gurara Falls is a major scenic waterfall and popular tourist attraction in central Nigeria, renowned for its impressive cascades and natural beauty.
  • E. Lugard Falls
    Lugard Falls is a series of spectacular white-water rapids and eroded rock formations on the Galana River in Kenya, known for its dramatic scenery and wildlife viewing.
  • 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_69ca8437a7ac81908651de48f2d2141d completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd7e61a114819081fc4a2ad39c96ba completed April 1, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1104514d481908d7cb9a87a01f1e2 completed April 4, 2026, 1:21 p.m.
Created at: March 30, 2026, 7:49 p.m.