Triple
T7794642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nzega |
E180267
|
entity |
| Predicate | governingCountryCapital |
P204
|
FINISHED |
| Object | Dodoma |
E107054
|
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: Dodoma | Statement: [Nzega, governingCountryCapital, Dodoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dodoma Context triple: [Nzega, governingCountryCapital, Dodoma]
-
A.
Dodoma
chosen
Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
-
B.
Dar es Salaam
Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
-
C.
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.
-
D.
Dodoma Region
Dodoma Region is an administrative region in central Tanzania that includes the national capital city, Dodoma.
-
E.
Nairobi
Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
- 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cae93b262c8190b55e5ab2bc72d894 |
completed | March 30, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb13ea96cc819081ac26db3ecf4481 |
completed | March 31, 2026, 12:23 a.m. |
Created at: March 30, 2026, 4:31 p.m.