Triple

T11137613
Position Surface form Disambiguated ID Type / Status
Subject Sarah Onyango Obama E263454 entity
Predicate placeOfDeath P21 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: [Sarah Onyango Obama, placeOfDeath, Kisumu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kisumu
Context triple: [Sarah Onyango Obama, placeOfDeath, 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_69d6aa9c0ba08190bbd19c217489b755 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e85f2ea48190bf1ff63af1d7d236 completed April 9, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69e442030b588190bc958030f30b648a completed April 19, 2026, 2:46 a.m.
Created at: April 8, 2026, 9:28 p.m.