Triple

T3212179
Position Surface form Disambiguated ID Type / Status
Subject Ngonye Falls E67304 entity
Predicate alsoKnownAs P39 FINISHED
Object Sioma Falls E67304 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: Sioma Falls | Statement: [Ngonye Falls, alsoKnownAs, Sioma Falls]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sioma Falls
Context triple: [Ngonye Falls, alsoKnownAs, Sioma Falls]
  • A. Itanda Falls
    Itanda Falls is a powerful series of white-water rapids on the Victoria Nile in Uganda, popular for rafting and kayaking.
  • B. Mutarazi Falls
    Mutarazi Falls is one of Zimbabwe’s tallest and most spectacular waterfalls, plunging dramatically down the edge of the Eastern Highlands.
  • C. Uhuru Falls
    Uhuru Falls is a powerful waterfall in northwestern Uganda located near Murchison Falls on the Nile River, known for its dramatic cascades and scenic surroundings.
  • D. Ngonye Falls chosen
    Ngonye Falls is a broad, horseshoe-shaped waterfall on the Zambezi River in western Zambia, noted for its scenic beauty and less-developed, remote setting compared to Victoria Falls.
  • 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_69ad858ac36c81909962589cd277d6e2 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaaba224c8190ad2f4e0ed1c2ca4a completed March 8, 2026, 4:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2771204e0819086ae2838a368589a completed March 12, 2026, 8:19 a.m.
Created at: March 8, 2026, 3:07 p.m.