Triple
T13672231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rayvanny |
E327778
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Mbeya, Tanzania |
E637653
|
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: Mbeya, Tanzania | Statement: [Rayvanny, placeOfBirth, Mbeya, Tanzania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mbeya, Tanzania Context triple: [Rayvanny, placeOfBirth, Mbeya, Tanzania]
-
A.
Arusha, Tanzania
Arusha, Tanzania is a major city in northern Tanzania known as a diplomatic hub and gateway to popular safari destinations and Mount Kilimanjaro.
-
B.
Mbeya
chosen
Mbeya is a major city in southwestern Tanzania, serving as a commercial and transport hub near the Zambian border.
-
C.
Nyamwezi
Nyamwezi is a Bantu language spoken primarily in northwestern Tanzania by the Nyamwezi people.
-
D.
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.
-
E.
Sari, Tanzania
Sari, Tanzania is a village in northern Tanzania, likely situated in the Kilimanjaro or Arusha region, known as a small rural settlement within the country.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65aab348190a6611f5765f8392d |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8c8e7988190bcd338bdeba0ae60 |
completed | May 3, 2026, 9:06 p.m. |
Created at: April 9, 2026, 9:53 p.m.