Triple
T5366930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Couto |
E103151
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Maputo, Mozambique |
E70168
|
NE FINISHED |
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: Maputo, Mozambique | Statement: [Mia Couto, residence, Maputo, Mozambique]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maputo, Mozambique Context triple: [Mia Couto, residence, Maputo, Mozambique]
-
A.
Maputo
chosen
Maputo is the largest city and main economic and cultural center of Mozambique, located on the country’s southern coast along the Indian Ocean.
-
B.
Manzini
Manzini is a major city in Eswatini that serves as an important commercial and transport hub of the country.
-
C.
Bulawayo
Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
-
D.
Maseru
Maseru is the largest city and administrative, economic, and cultural center of the Kingdom of Lesotho in southern Africa.
-
E.
Kinondoni
Kinondoni is a major urban district within Dar es Salaam, Tanzania, known for its dense population, commercial activity, and diverse residential neighborhoods.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43daa3e4819090b59d127db70e57 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd8684103081908ed79625b59e4b24 |
completed | March 20, 2026, 5:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf334553148190a5c53bda473c17c3 |
completed | March 22, 2026, 12:09 a.m. |
Created at: March 20, 2026, 2:02 p.m.