Triple
T1565828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tabora Region |
E33430
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Tabora |
E180266
|
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: Tabora | Statement: [Tabora Region, largestCity, Tabora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabora Context triple: [Tabora Region, largestCity, Tabora]
-
A.
Tabora
chosen
Tabora is a historic town in western Tanzania known as a regional trade center and former hub of 19th-century caravan routes.
-
B.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
C.
Mpanda
Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
-
D.
Matadi
Matadi is a major port city in western Democratic Republic of the Congo, serving as the country’s principal seaport and a key gateway for trade between the Atlantic Ocean and the interior via the Congo River.
-
E.
Tabora Region
Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
- 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_69a885f11b048190935025a035302715 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb2308bec81909d1660934eff171b |
completed | March 7, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51af2de8819087d287d65aabbf1a |
completed | March 8, 2026, 10:38 a.m. |
Created at: March 4, 2026, 7:27 p.m.