Triple

T23363948
Position Surface form Disambiguated ID Type / Status
Subject Swart E593260 entity
Predicate hasGeographicUsage P908 FINISHED
Object Namibia NE NERFINISHED

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: Namibia | Statement: [Swart, hasGeographicUsage, Namibia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Namibia
Context triple: [Swart, hasGeographicUsage, Namibia]
  • A. Namibia chosen
    Namibia is a sparsely populated country in southwestern Africa known for its dramatic desert landscapes, diverse wildlife, and a legal system influenced by Roman-Dutch law.
  • B. Botswana
    Botswana is a landlocked country in Southern Africa known for its stable democracy, significant diamond resources, and vast wildlife-rich landscapes including the Okavango Delta.
  • C. Namibia and Botswana
    Namibia and Botswana are neighboring countries in Southern Africa known for their vast deserts, rich wildlife, and major river systems that shape their shared ecosystems and borders.
  • D. Angola and Namibia
    Angola and Namibia are neighboring countries in southwestern Africa that share a long land border and diverse ecosystems, including parts of the Kavango–Zambezi conservation region.
  • E. Tiszanána
    Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d2593c88190bcdf4a716a94ccb2 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1a0aac4248190a4663ed12aed6856 completed April 29, 2026, 6:09 a.m.
Created at: April 17, 2026, 5:31 p.m.