Triple
T21934166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prime Minister of Lesotho |
E541645
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Maseru |
—
|
NE NERFINISHED |
How this triple was built (2 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: Maseru | Statement: [Prime Minister of Lesotho, seat, Maseru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maseru Context triple: [Prime Minister of Lesotho, seat, Maseru]
-
A.
Maseru
chosen
Maseru is the largest city and administrative, economic, and cultural center of the Kingdom of Lesotho in southern Africa.
-
B.
Gaborone
Gaborone is the capital and largest city of Botswana, serving as its political and economic center.
-
C.
Maputo
Maputo is the largest city and main economic and cultural center of Mozambique, located on the country’s southern coast along the Indian Ocean.
-
D.
Okahandja
Okahandja is a town in central Namibia known as a historical center of the Herero people and an important stop on the main route between Windhoek and the northern regions.
-
E.
Gqeberha
Gqeberha is a major coastal city in South Africa’s Eastern Cape, known as an important industrial, commercial, and port hub.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12402f7ac81909b14586a46d971bb |
completed | April 28, 2026, 9:17 p.m. |
Created at: April 16, 2026, 7:49 p.m.