Triple
T22618025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Savannah Region |
E558198
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Larabanga
Larabanga is a historic village in northern Ghana best known for its ancient mud-and-stick mosque, one of the oldest Islamic structures in West Africa.
|
E1546734
|
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: Larabanga | Statement: [Savannah Region, hasSettlement, Larabanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larabanga Context triple: [Savannah Region, hasSettlement, Larabanga]
-
A.
Kaolack
Kaolack is a major city in western Senegal known as a regional commercial hub and center of peanut trade.
-
B.
Bignona
Bignona is a town in southern Senegal’s Casamance region, known as a local center of trade and cultural diversity.
-
C.
Djenné Songhay
Djenné Songhay is a regional variety of the Songhay language spoken around the town of Djenné in Mali.
-
D.
Djenné
Djenné is an ancient Malian town renowned for its mud-brick architecture and historic Great Mosque, a UNESCO World Heritage site and one of the most famous examples of Sudano-Sahelian architecture.
-
E.
Ambouli
Ambouli is a district of Djibouti City that hosts the country’s main international airport and related urban infrastructure.
- 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: Larabanga Triple: [Savannah Region, hasSettlement, Larabanga]
Generated description
Larabanga is a historic village in northern Ghana best known for its ancient mud-and-stick mosque, one of the oldest Islamic structures in West Africa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Larabanga Target entity description: Larabanga is a historic village in northern Ghana best known for its ancient mud-and-stick mosque, one of the oldest Islamic structures in West Africa.
-
A.
Kaolack
Kaolack is a major city in western Senegal known as a regional commercial hub and center of peanut trade.
-
B.
Bignona
Bignona is a town in southern Senegal’s Casamance region, known as a local center of trade and cultural diversity.
-
C.
Djenné Songhay
Djenné Songhay is a regional variety of the Songhay language spoken around the town of Djenné in Mali.
-
D.
Djenné
Djenné is an ancient Malian town renowned for its mud-brick architecture and historic Great Mosque, a UNESCO World Heritage site and one of the most famous examples of Sudano-Sahelian architecture.
-
E.
Ambouli
Ambouli is a district of Djibouti City that hosts the country’s main international airport and related urban infrastructure.
- 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_69e24545a8e08190bfa7482a2c725ff1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f167ef7a148190870334af9c8b79a4 |
completed | April 29, 2026, 2:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b4e018e04819086ec5d885fb76f67 |
completed | May 18, 2026, 5:36 p.m. |
| NEDg | Description generation | batch_6a0b513c2908819087b05efdbf8688aa |
completed | May 18, 2026, 5:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b51db01948190b968a3f289419776 |
completed | May 18, 2026, 5:52 p.m. |
Created at: April 17, 2026, 2:59 p.m.