Triple

T10263933
Position Surface form Disambiguated ID Type / Status
Subject Pan-African Congress E240668 entity
Predicate hasLocation P40 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: [Pan-African Congress, hasLocation, Kisumu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kisumu
Context triple: [Pan-African Congress, hasLocation, 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. Kabete
    Kabete is a prominent town in Kenya’s Central Region, situated within Kiambu County and known for its agricultural activity and proximity to Nairobi.
  • C. 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.
  • D. Eldoret
    Eldoret is a major town in western Kenya known as an agricultural and commercial hub and as a center for world-class long-distance runners.
  • E. Nyamira
    Nyamira is a town in western Kenya that serves as an administrative and commercial center in the former Nyanza region.
  • 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_69d381a94c1881908fc38fc263d9b9c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d25e68fc8190b46699d2266c0505 completed April 7, 2026, 9:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f7ff3f808190b4a8a021f44e2176 completed April 9, 2026, 12:51 a.m.
Created at: April 6, 2026, 11:33 a.m.