Triple
T5909345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akure |
E131420
|
entity |
| Predicate | hasLocalGovernmentArea |
P8215
|
FINISHED |
| Object |
Akure South
Akure South is a local government area in Ondo State, Nigeria, that encompasses much of the urban and administrative core of the city of Akure.
|
E553910
|
NE FINISHED |
How this triple was built (4 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 South | Statement: [Akure, hasLocalGovernmentArea, Akure South]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akure South Context triple: [Akure, hasLocalGovernmentArea, Akure South]
-
A.
Akure
Akure is the capital city of Ondo State in southwestern Nigeria, known as an important administrative and commercial center in the region.
-
B.
Nordre Land
Nordre Land is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and agricultural landscape in the traditional district of Land.
-
C.
Helgeland
Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
-
D.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
-
E.
Hadeland
Hadeland is a traditional rural district in southeastern Norway known for its agricultural landscape, historic churches, and the Hadeland Glassverk glassworks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Akure South Triple: [Akure, hasLocalGovernmentArea, Akure South]
Generated description
Akure South is a local government area in Ondo State, Nigeria, that encompasses much of the urban and administrative core of the city of Akure.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Akure South Target entity description: Akure South is a local government area in Ondo State, Nigeria, that encompasses much of the urban and administrative core of the city of Akure.
-
A.
Akure
Akure is the capital city of Ondo State in southwestern Nigeria, known as an important administrative and commercial center in the region.
-
B.
Nordre Land
Nordre Land is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and agricultural landscape in the traditional district of Land.
-
C.
Helgeland
Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
-
D.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
-
E.
Hadeland
Hadeland is a traditional rural district in southeastern Norway known for its agricultural landscape, historic churches, and the Hadeland Glassverk glassworks.
- F. None of above. chosen
Provenance (5 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03775590481909a797b166fbe108c |
completed | March 22, 2026, 6:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b17375488190a3053d37712501b3 |
completed | March 23, 2026, 3:20 a.m. |
| NEDg | Description generation | batch_69c0b314b814819087be63d41c10e26e |
completed | March 23, 2026, 3:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0b3c1e9c08190bc291e1e20005aba |
completed | March 23, 2026, 3:30 a.m. |
Created at: March 22, 2026, 3:59 p.m.