Triple
T3108230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kwara State |
E64885
|
entity |
| Predicate | hasLocalGovernmentArea |
P8215
|
FINISHED |
| Object |
Isin
Isin is a local government area in Kwara State, Nigeria, known for its predominantly Yoruba communities and agrarian economy.
|
E326939
|
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: Isin | Statement: [Kwara State, hasLocalGovernmentArea, Isin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isin Context triple: [Kwara State, hasLocalGovernmentArea, Isin]
-
A.
Isanzu
Isanzu is a Bantu language spoken by the Isanzu people of north-central Tanzania.
-
B.
Ihnasya
Ihnasya is a city in Egypt known for its location within the Beni Suef Governorate along the Nile Valley.
-
C.
Beni
Beni is a town in western Nepal that serves as a gateway to the Dhaulagiri and Annapurna mountain regions.
-
D.
Beni
Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
-
E.
Beni
Beni is a city in the eastern Democratic Republic of the Congo that became internationally known as a major hotspot of conflict and public health crises, including serving as the epicenter of the 2018–2020 Kivu Ebola epidemic.
- 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: Isin Triple: [Kwara State, hasLocalGovernmentArea, Isin]
Generated description
Isin is a local government area in Kwara State, Nigeria, known for its predominantly Yoruba communities and agrarian economy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Isin Target entity description: Isin is a local government area in Kwara State, Nigeria, known for its predominantly Yoruba communities and agrarian economy.
-
A.
Isanzu
Isanzu is a Bantu language spoken by the Isanzu people of north-central Tanzania.
-
B.
Ihnasya
Ihnasya is a city in Egypt known for its location within the Beni Suef Governorate along the Nile Valley.
-
C.
Beni
Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
-
D.
Beni
Beni is a city in the eastern Democratic Republic of the Congo that became internationally known as a major hotspot of conflict and public health crises, including serving as the epicenter of the 2018–2020 Kivu Ebola epidemic.
-
E.
Beni
Beni is a town in western Nepal that serves as a gateway to the Dhaulagiri and Annapurna mountain regions.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada29eacc88190a19c5ca8e53e3dca |
completed | March 8, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2038c89248190b880108c82ad35b1 |
completed | March 12, 2026, 12:06 a.m. |
| NEDg | Description generation | batch_69b2046f76488190adef6685544b080e |
completed | March 12, 2026, 12:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2054bca388190ad40b2303ac96373 |
completed | March 12, 2026, 12:14 a.m. |
Created at: March 8, 2026, 3:04 p.m.