Triple

T14227310
Position Surface form Disambiguated ID Type / Status
Subject ZESCO E352655 entity
Predicate headquartersLocation P62 FINISHED
Object Lusaka E31817 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: Lusaka | Statement: [ZESCO, headquartersLocation, Lusaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lusaka
Context triple: [ZESCO, headquartersLocation, Lusaka]
  • A. Lusaka, Zambia chosen
    Lusaka, Zambia is the capital and largest city of Zambia, serving as the country’s political, economic, and cultural center.
  • B. Lilongwe
    Lilongwe is the largest city and administrative and political center of Malawi, located in the country’s central region.
  • C. Matadi
    Matadi is a major port city in western Democratic Republic of the Congo, serving as the country’s principal seaport and a key gateway for trade between the Atlantic Ocean and the interior via the Congo River.
  • D. Lubumbashi
    Lubumbashi is the second-largest city in the Democratic Republic of the Congo and a major mining and commercial center in the southeastern part of the country.
  • E. Kasane
    Kasane is a small town in northern Botswana that serves as a key gateway and service hub for visitors to Chobe National Park and the surrounding wildlife areas.
  • 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_69d8278adc7c8190a9218d69bce3c4e6 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de622a48508190bbfedb762bd1674d completed April 14, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd281801488190bcb17d27ee18cde6 completed May 8, 2026, 12:02 a.m.
Created at: April 10, 2026, 1:07 a.m.