Triple

T10587555
Position Surface form Disambiguated ID Type / Status
Subject College of Humanities and Social Sciences, Makerere University E249892 entity
Predicate city P40 FINISHED
Object Kampala E40695 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: Kampala | Statement: [College of Humanities and Social Sciences, Makerere University, city, Kampala]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kampala
Context triple: [College of Humanities and Social Sciences, Makerere University, city, Kampala]
  • A. Kampala chosen
    Kampala is the capital and largest city of Uganda, serving as the country’s political, economic, and cultural center.
  • B. Entebbe
    Entebbe is a town in central Uganda on a peninsula into Lake Victoria, known for its international airport and the site of the 1976 hostage-rescue operation.
  • C. Dodoma
    Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
  • D. Lipa City
    Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
  • E. 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.
  • 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_69d381c9d3d48190a29ee491e1696a0e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5276b0ae48190b2935230363239e0 completed April 7, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b9440548190bff01847a940266b completed April 10, 2026, 7:12 p.m.
Created at: April 6, 2026, 12:39 p.m.