Triple

T249683
Position Surface form Disambiguated ID Type / Status
Subject Kigoma Region E5115 entity
Predicate hasTown P847 FINISHED
Object Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
 E36568 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: Kibondo | Statement: [Kigoma Region, hasTown, Kibondo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kibondo
Context triple: [Kigoma Region, hasTown, Kibondo]
  • A. Kigoma
    Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
  • B. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • C. Kiunguja
    Kiunguja is the Zanzibar-based variety of Swahili that historically served as the primary basis for the standardized form of the language.
  • D. Talla Keyali
    Talla Keyali is a Xelayan security officer on the science fiction comedy-drama series "The Orville," known for her superhuman strength and role on the ship’s senior staff.
  • E. Beni
    Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
  • 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: Kibondo
Triple: [Kigoma Region, hasTown, Kibondo]
Generated description
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kibondo
Target entity description: Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • A. Kigoma
    Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
  • B. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • C. Kiunguja
    Kiunguja is the Zanzibar-based variety of Swahili that historically served as the primary basis for the standardized form of the language.
  • D. Talla Keyali
    Talla Keyali is a Xelayan security officer on the science fiction comedy-drama series "The Orville," known for her superhuman strength and role on the ship’s senior staff.
  • E. Beni
    Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
  • 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_69a257c4bf688190a46ebbf411ab7473 completed Feb. 28, 2026, 2:49 a.m.
NER Named-entity recognition batch_69a25d3728f0819086214ccc2db2305a completed Feb. 28, 2026, 3:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69a399a2991c8190a3f79aa899720a7c completed March 1, 2026, 1:42 a.m.
NEDg Description generation batch_69a39afffa5c8190a71e91cbea794197 completed March 1, 2026, 1:48 a.m.
NED2 Entity disambiguation (via description) batch_69a39b7aac5881908b2efeaae2603555 completed March 1, 2026, 1:50 a.m.
Created at: Feb. 28, 2026, 2:54 a.m.