Triple
T8278119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serer |
E193597
|
entity |
| Predicate | hasDialects |
P4251
|
FINISHED |
| Object | Serer-Noon |
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-Noon | Statement: [Serer, hasDialects, Serer-Noon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serer-Noon Context triple: [Serer, hasDialects, Serer-Noon]
-
A.
Serer
chosen
Serer is a Niger–Congo language spoken primarily by the Serer people of Senegal and neighboring regions.
-
B.
Son Servera
Son Servera is a coastal municipality and village on the eastern side of the island of Mallorca in Spain’s Balearic Islands.
-
C.
Serein
Serein is a river in central France that flows through the Burgundy region before joining the Yonne River.
-
D.
Nesuhi
Nesuhi was a prominent Turkish-American record producer and music executive best known for his influential work in jazz, particularly at Atlantic Records.
-
E.
Saravena
Saravena is a Colombian town and municipality located in the northeastern oil-producing and conflict-affected region near the border with Venezuela.
- 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_69ca82e217a48190880695635c44b2ed |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb79ebb6b88190bc777b8bd72fcdbc |
completed | March 31, 2026, 7:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd6863c22c8190b888a23bb9005712 |
completed | April 1, 2026, 6:48 p.m. |
Created at: March 30, 2026, 5:51 p.m.