Triple
T576229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Translate |
E13763
|
entity |
| Predicate | availableLanguage |
P2177
|
FINISHED |
| Object | Kikuyu |
E53602
|
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: Kikuyu | Statement: [Google Translate, availableLanguage, Kikuyu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kikuyu Context triple: [Google Translate, availableLanguage, Kikuyu]
-
A.
Kikuyu
chosen
Kikuyu is a major Bantu language spoken primarily by the Kikuyu people of central Kenya.
-
B.
Murrurundi
Murrurundi is a small rural town in New South Wales, Australia, known for its scenic setting in the Upper Hunter region and its historic buildings.
-
C.
Yamba
Yamba is a coastal town in northern New South Wales, Australia, known for its beaches, fishing, and laid-back holiday atmosphere.
-
D.
Casuarina
Casuarina is a coastal northern suburb of Darwin in Australia's Northern Territory, known for its major shopping centre and popular beach.
-
E.
Lucerne
Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b67395c8190a8046ff7debe9d1f |
completed | March 1, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4ff4db0248190b2b3ca0290467313 |
completed | March 2, 2026, 3:09 a.m. |
Created at: March 1, 2026, 7:33 p.m.