Triple
T3024338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Nigeria |
E82540
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Akure |
E131420
|
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: Akure | Statement: [Southern Nigeria, contains, Akure]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akure Context triple: [Southern Nigeria, contains, Akure]
-
A.
Akure
chosen
Akure is the capital city of Ondo State in southwestern Nigeria, known as an important administrative and commercial center in the region.
-
B.
Storo
Storo is a neighborhood and transport hub in Oslo, Norway, known for its major shopping center and connections to tram, metro, and bus lines.
-
C.
Ballstad
Ballstad is a fishing village in Norway’s Lofoten archipelago, known for its scenic coastal landscape and traditional maritime culture.
-
D.
Ulsta
Ulsta is a small settlement on the island of Yell in Shetland, Scotland, known primarily for its ferry terminal linking Yell to the Shetland mainland.
-
E.
Seskarö
Seskarö is a Swedish island in the northern Baltic Sea known for its forests, beaches, and traditional fishing and forestry communities.
- 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_69ad8b1fb34081908c1b873e2b7273e1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9abac0288190a30b42674eea501f |
completed | March 8, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1deb342008190bec8fbaa4074d40c |
completed | March 11, 2026, 9:29 p.m. |
Created at: March 8, 2026, 3 p.m.