Triple

T8585706
Position Surface form Disambiguated ID Type / Status
Subject Buganda E203300 entity
Predicate contains P35 FINISHED
Object Kiboga
Kiboga is a town and district in central Uganda, located within the traditional kingdom of Buganda.
 E745081 NE FINISHED

How this triple was built (4 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: Kiboga | Statement: [Buganda, contains, Kiboga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kiboga
Context triple: [Buganda, contains, Kiboga]
  • A. Nakasongola
    Nakasongola is a town in central Uganda that serves as an administrative and commercial center for the surrounding rural district.
  • B. Kisoro
    Kisoro is a small town in southwestern Uganda known as a gateway to gorilla trekking and the nearby Bwindi Impenetrable and Mgahinga Gorilla National Parks.
  • C. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • D. Kalangoya
    Kalangoya is an alternative name for the Kalanguya language, an Austronesian language spoken by indigenous communities in the northern Philippines.
  • E. Monguno
    Monguno is a town and local government area in Borno State, northeastern Nigeria, known for its strategic location and role in regional security dynamics.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kiboga
Triple: [Buganda, contains, Kiboga]
Generated description
Kiboga is a town and district in central Uganda, located within the traditional kingdom of Buganda.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kiboga
Target entity description: Kiboga is a town and district in central Uganda, located within the traditional kingdom of Buganda.
  • A. Nakasongola
    Nakasongola is a town in central Uganda that serves as an administrative and commercial center for the surrounding rural district.
  • B. Kisoro
    Kisoro is a small town in southwestern Uganda known as a gateway to gorilla trekking and the nearby Bwindi Impenetrable and Mgahinga Gorilla National Parks.
  • C. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • D. Kalangoya
    Kalangoya is an alternative name for the Kalanguya language, an Austronesian language spoken by indigenous communities in the northern Philippines.
  • E. Monguno
    Monguno is a town and local government area in Borno State, northeastern Nigeria, known for its strategic location and role in regional security dynamics.
  • F. None of above. chosen

Provenance (5 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_69ca8329bb7c8190a63c643730839103 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc457ab8b08190a53c730417288deb completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea89e4658819090cc6e94e934670b completed April 2, 2026, 5:34 p.m.
NEDg Description generation batch_69cea9cff1ec8190a0093fb42782341e completed April 2, 2026, 5:39 p.m.
NED2 Entity disambiguation (via description) batch_69ceaa9f7f8c8190965e86880ff141d5 completed April 2, 2026, 5:42 p.m.
Created at: March 30, 2026, 6:22 p.m.