Triple
T2392368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaborone |
E48971
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Chief Gaborone
Chief Gaborone was a local leader of the BaTlokwa people in Botswana whose legacy is commemorated in the naming of the country’s capital city, Gaborone.
|
E263075
|
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: Chief Gaborone | Statement: [Gaborone, namedAfter, Chief Gaborone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chief Gaborone Context triple: [Gaborone, namedAfter, Chief Gaborone]
-
A.
Thabana Ntlenyana
Thabana Ntlenyana is the highest mountain in southern Africa, located in the Drakensberg range within Lesotho.
-
B.
Mpulungu
Mpulungu is a Zambian port town that serves as the country’s main access point to Lake Tanganyika and a hub for regional fishing and trade.
-
C.
Sabata Dalindyebo
Sabata Dalindyebo was a prominent South African traditional leader and king of the Thembu people who became known for his resistance to apartheid-era policies.
-
D.
Tshiphani
Tshiphani is a regional dialect of the Tshivenda language spoken by Venda communities in parts of southern Africa.
-
E.
Marondera
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
- 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: Chief Gaborone Triple: [Gaborone, namedAfter, Chief Gaborone]
Generated description
Chief Gaborone was a local leader of the BaTlokwa people in Botswana whose legacy is commemorated in the naming of the country’s capital city, Gaborone.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chief Gaborone Target entity description: Chief Gaborone was a local leader of the BaTlokwa people in Botswana whose legacy is commemorated in the naming of the country’s capital city, Gaborone.
-
A.
Thabana Ntlenyana
Thabana Ntlenyana is the highest mountain in southern Africa, located in the Drakensberg range within Lesotho.
-
B.
Mpulungu
Mpulungu is a Zambian port town that serves as the country’s main access point to Lake Tanganyika and a hub for regional fishing and trade.
-
C.
Sabata Dalindyebo
Sabata Dalindyebo was a prominent South African traditional leader and king of the Thembu people who became known for his resistance to apartheid-era policies.
-
D.
Tshiphani
Tshiphani is a regional dialect of the Tshivenda language spoken by Venda communities in parts of southern Africa.
-
E.
Marondera
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
- 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_69a88aa5f63081908d07fd302029fcbd |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc87587708190a7f2bc473a898bc2 |
completed | March 7, 2026, 6:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3d7b4908190a87dd33316d2d725 |
completed | March 9, 2026, 11:49 a.m. |
| NEDg | Description generation | batch_69aeb4b83ec48190b2852daef0767ac8 |
completed | March 9, 2026, 11:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeb57f0e90819093b096955f9cc2b5 |
completed | March 9, 2026, 11:56 a.m. |
Created at: March 4, 2026, 7:57 p.m.