Triple
T21815507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanga |
E538595
|
entity |
| Predicate | neighboringLanguage |
P16383
|
FINISHED |
| Object | Kokota |
—
|
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: Kokota | Statement: [Blanga, neighboringLanguage, Kokota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kokota Context triple: [Blanga, neighboringLanguage, Kokota]
-
A.
Kokota
chosen
Kokota is an Oceanic language spoken in the Solomon Islands, particularly on Santa Isabel Island.
-
B.
Kota
Kota is a major industrial and educational city in southeastern Rajasthan, India, known for its coaching institutes and power plants along the Chambal River.
-
C.
Oga City
Oga City is a coastal municipality in Akita Prefecture, Japan, known for its rugged Oga Peninsula landscapes and the traditional Namahage folklore.
-
D.
Kuito
Kuito is a city in central Angola that serves as the capital of Bié Province and a key hub on the country’s central plateau.
-
E.
Konikoni City
Konikoni City is a bustling port town in Pokémon Sun and Moon’s Alola region, known for its markets, lighthouse, and access to Akala Island’s coastal routes.
- 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_69e0c473f0f8819086c9d1b4a143bd67 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f07cc99bbc8190bf074930f361af7d |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 16, 2026, 6:54 p.m.