Triple
T12225634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Obio-Akpor |
E291343
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Rumuokoro |
E338071
|
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: Rumuokoro | Statement: [Obio-Akpor, hasSettlement, Rumuokoro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rumuokoro Context triple: [Obio-Akpor, hasSettlement, Rumuokoro]
-
A.
Rumuokoro
chosen
Rumuokoro is a bustling urban town and major commercial transport hub in Obio-Akpor, within the Port Harcourt metropolitan area of Rivers State, Nigeria.
-
B.
Abamakoro
Abamakoro is a small village located on the island of Nonouti in the Republic of Kiribati in the central Pacific Ocean.
-
C.
Tontemboan
Tontemboan is an Austronesian language spoken by the Tontemboan people in North Sulawesi, Indonesia.
-
D.
Dutsin-Ma
Dutsin-Ma is a town in northern Nigeria known for hosting the Federal University Dutsin-Ma and serving as an important local commercial and educational center.
-
E.
Koulamoutou
Koulamoutou is a town in central Gabon that serves as an administrative and economic hub for the surrounding region.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ca2101c8190955c36704935036a |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60aa9023881909f8373e02d2cad4b |
completed | May 2, 2026, 2:31 p.m. |
Created at: April 8, 2026, 9:51 p.m.