Triple

T3908531
Position Surface form Disambiguated ID Type / Status
Subject Nyanga National Park E87264 entity
Predicate near P350 FINISHED
Object Nyanga town E327528 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: Nyanga town | Statement: [Nyanga National Park, near, Nyanga town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nyanga town
Context triple: [Nyanga National Park, near, Nyanga town]
  • A. Nyanga District chosen
    Nyanga District is an administrative district in northeastern Zimbabwe known for its mountainous landscapes and popular tourist attractions such as Nyanga National Park.
  • B. Nyanga
    Nyanga is a township on the Cape Flats near Cape Town, South Africa, known for its history of apartheid-era resistance and ongoing social and economic challenges.
  • C. Kisumu
    Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
  • D. Kalangala
    Kalangala is a town on Uganda’s Ssese Islands in Lake Victoria, serving as the administrative and commercial center of Kalangala District.
  • E. Soroti
    Soroti is a town in eastern Uganda that serves as a regional commercial and administrative center.
  • 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_69aed9424514819086e9c58adde6652d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeed13bb14819096842c6c82342524 completed March 9, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51caf41c881909c5156480b46e794 completed March 14, 2026, 8:30 a.m.
Created at: March 9, 2026, 3:22 p.m.