Triple
T14050913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apa |
E338086
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object | Ugbokpo |
E1084767
|
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: Ugbokpo | Statement: [Apa, administrativeCenter, Ugbokpo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ugbokpo Context triple: [Apa, administrativeCenter, Ugbokpo]
-
A.
Ugbokpo
chosen
Ugbokpo is a town in Benue State, Nigeria, serving as the administrative headquarters of Apa Local Government Area.
-
B.
Ugbokolo
Ugbokolo is a town in Okpokwu Local Government Area of Benue State, Nigeria, known as a local commercial and educational center in the region.
-
C.
Igbesa
Igbesa is a prominent town in Ogun State, Nigeria, known for its growing industrial presence and educational institutions.
-
D.
Ogwashi-Ukwu
Ogwashi-Ukwu is a town in Delta State, southern Nigeria, known as the hometown of prominent economist and World Trade Organization Director-General Ngozi Okonjo-Iweala.
-
E.
Igbokoda
Igbokoda is a prominent riverine town in southwestern Nigeria known for its fishing activities and waterways.
- 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_69d81c664e48819088cbd8f433aeffe5 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de3c8a518081908ad030ba48b7b946 |
completed | April 14, 2026, 1:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd27fdad4c8190bf5ce5d676284e62 |
completed | May 8, 2026, 12:02 a.m. |
Created at: April 9, 2026, 10:20 p.m.