Triple

T11773194
Position Surface form Disambiguated ID Type / Status
Subject Kisolongo E279950 entity
Predicate glottologName P6521 FINISHED
Object Kisolongo E279950 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: Kisolongo | Statement: [Kisolongo, glottologName, Kisolongo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kisolongo
Context triple: [Kisolongo, glottologName, Kisolongo]
  • A. Kisolongo chosen
    Kisolongo is a regional dialect of the Kikongo language spoken by Kongo communities in parts of Central Africa.
  • B. Chilongolo
    Chilongolo is a locality in East Africa situated along the route of the Tanzania–Zambia Railway.
  • C. Mlolongo
    Mlolongo is a rapidly growing urban town in Kenya’s Machakos County, situated along the Nairobi–Mombasa highway and known for its bustling commercial activity and residential estates.
  • D. Zalongo
    Zalongo is a historic village and archaeological area in Epirus, Greece, best known for the 19th-century mass suicide of Souliot women commemorated by the Dance of Zalongo.
  • E. Kasangati
    Kasangati is a town in central Uganda that serves as a growing commercial and residential hub within the Greater Kampala metropolitan area.
  • 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_69d6ab01d2688190ad8ed6bda487eaa5 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a55dfa088190a59b35d0247225e3 completed April 10, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69f0909969e481908d836f912b5af5bf completed April 28, 2026, 10:48 a.m.
Created at: April 8, 2026, 9:41 p.m.