Triple

T3222284
Position Surface form Disambiguated ID Type / Status
Subject Morro de Môco E67537 entity
Predicate nearCity P350 FINISHED
Object Huambo E261453 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: Huambo | Statement: [Morro de Môco, nearCity, Huambo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Huambo
Context triple: [Morro de Môco, nearCity, Huambo]
  • A. Huambo chosen
    Huambo is a major city in central Angola that served as a strategic stronghold and frequent battleground during the Angolan Civil War.
  • B. Chinhoyi
    Chinhoyi is a town in northern Zimbabwe known as an administrative center and for the nearby Chinhoyi Caves.
  • C. Marondera
    Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
  • D. Moxico Province
    Moxico Province is the largest and one of the most war-affected provinces in eastern Angola, known for its strategic role and heavy fighting during the Angolan Civil War.
  • E. 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.
  • 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_69ad858b8adc8190ad989712c87a476b completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adae1845408190b3eccd791231c69c completed March 8, 2026, 5:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b27726f76c819092a199ae07a7e688 completed March 12, 2026, 8:19 a.m.
Created at: March 8, 2026, 3:08 p.m.