Triple
T249681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kigoma Region |
E5115
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
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.
|
E34026
|
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: Kigoma | Statement: [Kigoma Region, hasCity, Kigoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kigoma Context triple: [Kigoma Region, hasCity, Kigoma]
-
A.
Kigoma Region
Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
-
B.
Tabora Region
Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
-
C.
Mutare
Mutare is a major city in eastern Zimbabwe, serving as the capital of Manicaland Province and an important commercial and transport hub near the border with Mozambique.
-
D.
Masvingo
Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
-
E.
Kiunguja
Kiunguja is the Zanzibar-based variety of Swahili that historically served as the primary basis for the standardized form of the language.
- 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: Kigoma Triple: [Kigoma Region, hasCity, Kigoma]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kigoma Target entity description: 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.
-
A.
Kigoma Region
Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
-
B.
Tabora Region
Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
-
C.
Mutare
Mutare is a major city in eastern Zimbabwe, serving as the capital of Manicaland Province and an important commercial and transport hub near the border with Mozambique.
-
D.
Masvingo
Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
-
E.
Kiunguja
Kiunguja is the Zanzibar-based variety of Swahili that historically served as the primary basis for the standardized form of the language.
- 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_69a386155c0c8190baa1a1d5df15392e |
completed | March 1, 2026, 12:19 a.m. |
| NEDg | Description generation | batch_69a3867100008190a4d17f099802864c |
completed | March 1, 2026, 12:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a386c0ee288190ae58373c531e6313 |
completed | March 1, 2026, 12:22 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.