Triple
T6160101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nggela language |
E137417
|
entity |
| Predicate | glottologName |
P6521
|
FINISHED |
| Object | Nggela |
E574111
|
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: Nggela | Statement: [Nggela language, glottologName, Nggela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nggela Context triple: [Nggela language, glottologName, Nggela]
-
A.
Nggela
chosen
Nggela is an Oceanic language spoken by the Nggela (Florida) Islands communities in the Central Province of the Solomon Islands.
-
B.
Biloela
Biloela is a rural town in Queensland, Australia, known as an agricultural and administrative centre for the surrounding Central Queensland region.
-
C.
Girga
Girga is an ancient town in Upper Egypt, historically significant as a regional center along the Nile.
-
D.
Shiyali
Shiyali is the given name of S. R. Ranganathan, the influential Indian mathematician and librarian known as the father of library science in India.
-
E.
Shiyali
Shiyali is a town in the Indian state of Tamil Nadu, historically known as the birthplace of pioneering librarian and mathematician S. R. Ranganathan.
- 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_69c008a54fc88190b6ce4416490ca79d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d3445dc8190822954cee90f0dd7 |
completed | March 22, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16edb92048190ba47571e28db3cb6 |
completed | March 23, 2026, 4:48 p.m. |
Created at: March 22, 2026, 4:17 p.m.