Triple

T8701302
Position Surface form Disambiguated ID Type / Status
Subject Kololo Hill E206537 entity
Predicate locatedNear P294 FINISHED
Object Nakasero
Nakasero is a central and upscale neighborhood in Kampala, Uganda, known for its government offices, embassies, hotels, and commercial centers.
 E752493 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: Nakasero | Statement: [Kololo Hill, locatedNear, Nakasero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nakasero
Context triple: [Kololo Hill, locatedNear, Nakasero]
  • A. Nguru
    Nguru is a town and local government area in northeastern Nigeria known as a commercial hub and railway terminus in Yobe State.
  • B. Litohoro
    Litohoro is a town in northern Greece situated at the base of Mount Olympus, known as a gateway for hikers and visitors to the famous mountain.
  • C. Takutea
    Takutea is an uninhabited coral atoll in the Cook Islands known for its important seabird nesting colonies and traditional conservation practices.
  • D. Sikaiana
    Sikaiana is a small, remote Polynesian atoll in the Solomon Islands whose people and culture are part of the Polynesian outlier communities in Melanesia.
  • E. Ahangama
    Ahangama is a coastal town in southern Sri Lanka known for its beaches, surfing spots, and traditional stilt fishermen.
  • 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: Nakasero
Triple: [Kololo Hill, locatedNear, Nakasero]
Generated description
Nakasero is a central and upscale neighborhood in Kampala, Uganda, known for its government offices, embassies, hotels, and commercial centers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nakasero
Target entity description: Nakasero is a central and upscale neighborhood in Kampala, Uganda, known for its government offices, embassies, hotels, and commercial centers.
  • A. Nguru
    Nguru is a town and local government area in northeastern Nigeria known as a commercial hub and railway terminus in Yobe State.
  • B. Litohoro
    Litohoro is a town in northern Greece situated at the base of Mount Olympus, known as a gateway for hikers and visitors to the famous mountain.
  • C. Takutea
    Takutea is an uninhabited coral atoll in the Cook Islands known for its important seabird nesting colonies and traditional conservation practices.
  • D. Sikaiana
    Sikaiana is a small, remote Polynesian atoll in the Solomon Islands whose people and culture are part of the Polynesian outlier communities in Melanesia.
  • E. Ahangama
    Ahangama is a coastal town in southern Sri Lanka known for its beaches, surfing spots, and traditional stilt fishermen.
  • 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_69ca83555b6c8190abe930dd397e863b completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc58b38cf88190bfdcbac9c340cb96 completed March 31, 2026, 11:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf28a0dd708190b12872883a2276c8 completed April 3, 2026, 2:40 a.m.
NEDg Description generation batch_69cf2bcff84881908a7985fdf8189583 completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2ca1ddac8190a36367e6bba8e3c8 completed April 3, 2026, 2:57 a.m.
Created at: March 30, 2026, 6:34 p.m.