Triple
T14208955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gombe State |
E352175
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Gombe |
E61525
|
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: Gombe | Statement: [Gombe State, capital, Gombe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gombe Context triple: [Gombe State, capital, Gombe]
-
A.
Gombe
Gombe is a region in western Tanzania best known for its national park where pioneering primatologist Jane Goodall conducted her landmark chimpanzee research.
-
B.
Gombe
chosen
Gombe is a major city in northeastern Nigeria that serves as the capital and economic hub of Gombe State.
-
C.
Mbewuleni
Mbewuleni is a rural village in South Africa’s Eastern Cape province, best known as the birthplace of former South African president Thabo Mbeki.
-
D.
Nyala
Nyala is a major city in western Sudan that serves as a key commercial and administrative center in the Darfur region.
-
E.
Mambasa
Mambasa is a town and administrative center located in the forested Ituri region of northeastern Democratic Republic of the Congo.
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61fa8d24819092a8ec5d34c1c799 |
completed | April 14, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19557f908190abb3dc116676f215 |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:05 a.m.