Triple

T15141829
Position Surface form Disambiguated ID Type / Status
Subject Judiciary of Botswana E361701 entity
Predicate capitalSeat P8146 FINISHED
Object Gaborone E48971 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: Gaborone | Statement: [Judiciary of Botswana, capitalSeat, Gaborone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gaborone
Context triple: [Judiciary of Botswana, capitalSeat, Gaborone]
  • A. Gaborone chosen
    Gaborone is the capital and largest city of Botswana, serving as its political and economic center.
  • B. Maseru
    Maseru is the largest city and administrative, economic, and cultural center of the Kingdom of Lesotho in southern Africa.
  • C. Bulawayo
    Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
  • D. Tshwane
    Tshwane is a major metropolitan area in South Africa that includes the country’s administrative capital, Pretoria, and serves as an important political and economic hub.
  • E. Maputo
    Maputo is the largest city and main economic and cultural center of Mozambique, located on the country’s southern coast along the Indian Ocean.
  • 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_69d85a0759908190b8a051d2e2a1cbe6 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e005c5c4248190b57234e3ccf2831b completed April 15, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69febfee0ae48190a36523d3eeae9740 completed May 9, 2026, 5:02 a.m.
Created at: April 10, 2026, 3:07 a.m.