Triple
T13738695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senegambia |
E330021
|
entity |
| Predicate | majorEthnicGroups |
P52368
|
FINISHED |
| Object | Serer |
E193597
|
NE FINISHED |
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: Serer | Statement: [Senegambia, majorEthnicGroups, Serer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serer Context triple: [Senegambia, majorEthnicGroups, Serer]
-
A.
Serer
chosen
Serer is a Niger–Congo language spoken primarily by the Serer people of Senegal and neighboring regions.
-
B.
Serua
Serua is a small volcanic island in Indonesia’s Banda Sea, known for its steep terrain, active geology, and remote location within the Banda Arc.
-
C.
Son Servera
Son Servera is a coastal municipality and village on the eastern side of the island of Mallorca in Spain’s Balearic Islands.
-
D.
Serabi
Serabi is a traditional Indonesian pancake-like cake made from rice flour and coconut milk, often served with sweet toppings or syrup.
-
E.
Seròs
Seròs is a municipality in the comarca of Segrià in Catalonia, northeastern Spain, known for its agricultural landscape along the Segre River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de0204d50c8190a5413cc9a1b26e14 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d6bce9881909209231f6dfcf9bf |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.