Triple
T9884429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King's African Rifles |
E180893
|
entity |
| Predicate | garrison |
P75
|
FINISHED |
| Object | Zomba |
E646487
|
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: Zomba | Statement: [King's African Rifles, garrison, Zomba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zomba Context triple: [King's African Rifles, garrison, Zomba]
-
A.
Zomba
chosen
Zomba is a historic city in southern Malawi that served as the country’s former capital and remains an important administrative and educational center.
-
B.
Chegutu
Chegutu is a town in central northern Zimbabwe known for its agricultural activities and gold mining.
-
C.
Mbeya
Mbeya is a major city in southwestern Tanzania, serving as a commercial and transport hub near the Zambian border.
-
D.
Tongayi
Tongayi is a Zimbabwean actor known for his roles in film and television, including appearances in international productions.
-
E.
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.
- 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_69ca828082cc8190a40f8d299caa6545 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb45549488190833200977d558e47 |
completed | April 2, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d7cc8e481909efdd1333ae740a3 |
completed | April 5, 2026, 7:21 a.m. |
Created at: March 30, 2026, 8:38 p.m.