Triple
T3309458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katsina State |
E69533
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
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.
|
E350192
|
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: Dutsin-Ma | Statement: [Katsina State, hasTown, Dutsin-Ma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dutsin-Ma Context triple: [Katsina State, hasTown, Dutsin-Ma]
-
A.
Benina
Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
-
B.
Rumuokoro
Rumuokoro is a bustling urban town and major commercial transport hub in Obio-Akpor, within the Port Harcourt metropolitan area of Rivers State, Nigeria.
-
C.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
D.
Wele-Nzas
Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
-
E.
Khondji
Khondji is the surname of Darius Khondji, a renowned cinematographer known for his visually distinctive work on international films.
- 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: Dutsin-Ma Triple: [Katsina State, hasTown, Dutsin-Ma]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dutsin-Ma Target entity description: 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.
-
A.
Benina
Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
-
B.
Rumuokoro
Rumuokoro is a bustling urban town and major commercial transport hub in Obio-Akpor, within the Port Harcourt metropolitan area of Rivers State, Nigeria.
-
C.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
D.
Wele-Nzas
Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
-
E.
Khondji
Khondji is the surname of Darius Khondji, a renowned cinematographer known for his visually distinctive work on international films.
- 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_69ad859f218081909458d2cebbf57565 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0e9f33c81909cff835a83e0a657 |
completed | March 8, 2026, 5:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b32507bc808190b9c3fce4c456b0aa |
completed | March 12, 2026, 8:41 p.m. |
| NEDg | Description generation | batch_69b3257f7d8081908efa606fb0a6e792 |
completed | March 12, 2026, 8:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b325dd73b08190bae32d7ffa582ade |
completed | March 12, 2026, 8:45 p.m. |
Created at: March 8, 2026, 3:11 p.m.