Triple
T23521449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larabanga Mystic Stone |
E574517
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Larabanga |
—
|
NE NERFINISHED |
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: Larabanga | Statement: [Larabanga Mystic Stone, locatedIn, Larabanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larabanga Context triple: [Larabanga Mystic Stone, locatedIn, Larabanga]
-
A.
Larabanga
chosen
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.
-
B.
Kaolack
Kaolack is a major city in western Senegal known as a regional commercial hub and center of peanut trade.
-
C.
Bignona
Bignona is a town in southern Senegal’s Casamance region, known as a local center of trade and cultural diversity.
-
D.
Djenné Songhay
Djenné Songhay is a regional variety of the Songhay language spoken around the town of Djenné in Mali.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bb3dcc8190ba9a2b35972b58d0 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1aa873ad48190a86807bd4f26df82 |
completed | April 29, 2026, 6:51 a.m. |
Created at: April 17, 2026, 6:08 p.m.