Triple

T2084719
Position Surface form Disambiguated ID Type / Status
Subject Luo language E45322 entity
Predicate spokenInCity P8343 FINISHED
Object Kisumu E43852 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: Kisumu | Statement: [Luo language, spokenInCity, Kisumu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kisumu
Context triple: [Luo language, spokenInCity, Kisumu]
  • A. Kisumu chosen
    Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
  • B. Nakuru
    Nakuru is a prominent Kenyan city in the Rift Valley region, known for its proximity to Lake Nakuru National Park and its role as an important agricultural and commercial center.
  • C. Embakasi
    Embakasi is a residential and industrial area in Nairobi, Kenya, known for hosting key infrastructure and serving as a major gateway corridor to the city.
  • D. Kigoma
    Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
  • E. Kadoma
    Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka 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_69a8891869c88190a02643e3bb746f59 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abba53d4488190a7d9eabcb6904e8e completed March 7, 2026, 5:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae273f8e3481908c45f1686072a95d completed March 9, 2026, 1:49 a.m.
Created at: March 4, 2026, 7:41 p.m.