Triple
T4726286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanganyika |
E104892
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Dar es Salaam |
E108100
|
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: Dar es Salaam | Statement: [Tanganyika, capital, Dar es Salaam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dar es Salaam Context triple: [Tanganyika, capital, Dar es Salaam]
-
A.
Dar es Salaam
chosen
Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
-
B.
Dodoma
Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
-
C.
Zanzibar City
Zanzibar City is the historic and administrative capital of Zanzibar, Tanzania, renowned for its UNESCO-listed Stone Town and rich Swahili, Arab, and colonial heritage.
-
D.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
E.
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.
- 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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6446b42081908e023979c9685730 |
completed | March 20, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4d7f0bc88190b2e5a2d6cfd16892 |
completed | March 21, 2026, 7:49 a.m. |
Created at: March 20, 2026, 1:18 p.m.