Triple

T10909768
Position Surface form Disambiguated ID Type / Status
Subject Chinhoyi E257663 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Karoi E672883 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: Karoi | Statement: [Chinhoyi, hasNearbySettlement, Karoi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karoi
Context triple: [Chinhoyi, hasNearbySettlement, Karoi]
  • A. Karoi chosen
    Karoi is a small agricultural and commercial town in northern Zimbabwe known as a service center for the surrounding tobacco-growing region.
  • B. Kolmanskop
    Kolmanskop is a famous ghost town in Namibia’s Namib Desert, once a prosperous German colonial diamond mining settlement now known for its sand-filled, abandoned buildings.
  • C. Rustenburg
    Rustenburg is a city in South Africa’s North West Province known for its mining industry and as one of the venues for the 2010 FIFA World Cup.
  • D. Hazyview
    Hazyview is a small South African town in Mpumalanga known as a gateway to Kruger National Park and the scenic attractions of the surrounding Lowveld.
  • E. Bela-Bela
    Bela-Bela is a South African town in Limpopo Province known for its natural hot mineral springs and tourism-focused resorts.
  • 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_69d6aa864ed88190818280ab6791d065 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d77068e5488190bbc881ebf51d6b2e completed April 9, 2026, 9:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69e1554fc61c8190a0354e2f24cb62e4 completed April 16, 2026, 9:31 p.m.
Created at: April 8, 2026, 9:22 p.m.