Triple
T21160995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanak languages |
E521438
|
entity |
| Predicate | includeLanguage |
P2177
|
FINISHED |
| Object | Nengone |
—
|
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: Nengone | Statement: [Kanak languages, includeLanguage, Nengone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nengone Context triple: [Kanak languages, includeLanguage, Nengone]
-
A.
Nengone
chosen
Nengone is an Austronesian language spoken primarily by the indigenous Kanak people on Maré Island in New Caledonia.
-
B.
Enenga
Enenga is a dialect of the Myene language spoken by communities in Gabon.
-
C.
Naden
Naden is a principal naval base area within Canadian Forces Base Esquimalt that serves as a key hub for Royal Canadian Navy operations and administration on the Pacific coast.
-
D.
Okonedo
Okonedo is the surname of Sophie Okonedo, a British actress known for her acclaimed performances in film, television, and theatre.
-
E.
N'Kono
N'Kono is the surname of Thomas N'Kono, a renowned Cameroonian former goalkeeper considered one of Africa's greatest footballers.
- 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_69e0b50d1ea481909c07e63c3ead9316 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72530e3388190a170c5a2a19dadfc |
completed | April 21, 2026, 7:20 a.m. |
Created at: April 16, 2026, 2:59 p.m.