Triple

T17989015
Position Surface form Disambiguated ID Type / Status
Subject Ovamboland E430317 entity
Predicate historicalInhabitants P3032 FINISHED
Object Ngandjera 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: Ngandjera | Statement: [Ovamboland, historicalInhabitants, Ngandjera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ngandjera
Context triple: [Ovamboland, historicalInhabitants, Ngandjera]
  • A. Niangala
    Niangala is a small rural locality in the New England region of New South Wales, Australia, known for its high-altitude farmland and cool climate.
  • B. Gatenga
    Gatenga is an urban sector within Kigali, Rwanda, known for its residential neighborhoods and local commercial activity.
  • C. Karibib
    Karibib is a small mining and transport town in central Namibia known for its marble quarries and strategic location between Windhoek and the coastal city of Swakopmund.
  • D. Ovambo chosen
    Ovambo is a Bantu language spoken primarily by the Ovambo people in northern Namibia and southern Angola.
  • E. Omaruru
    Omaruru is a small historic town in central Namibia known for its colonial-era architecture, vineyards, and role as a local trading and farming center.
  • 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_69d8b90364248190a37381adea932f42 completed April 10, 2026, 8:46 a.m.
NER Named-entity recognition batch_69e4b29e47a88190be58b79c73d3e652 completed April 19, 2026, 10:46 a.m.
Created at: April 10, 2026, 10:23 a.m.