Triple
T10924495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kisii District |
E258030
|
entity |
| Predicate | languageMajority |
P237
|
FINISHED |
| Object | Ekegusii |
E820926
|
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: Ekegusii | Statement: [Kisii District, languageMajority, Ekegusii]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ekegusii Context triple: [Kisii District, languageMajority, Ekegusii]
-
A.
Ekegusii
chosen
Ekegusii is a Bantu language spoken primarily by the Abagusii people of western Kenya.
-
B.
Akita
Akita is a city in Japan’s Tōhoku region, serving as the capital of Akita Prefecture and known for its port, rice production, and traditional festivals.
-
C.
Akita
Akita is a large, powerful Japanese dog breed known for its loyalty, dignity, and strong protective instincts.
-
D.
Ebisu
Ebisu is a fashionable Tokyo neighborhood known for its upscale dining, craft beer scene, and convenient access via Ebisu Station near Shibuya.
-
E.
Havu
Havu are a Bantu-speaking ethnic group primarily inhabiting the Lake Kivu region of eastern Democratic Republic of the Congo, known for their farming, fishing, and rich cultural traditions.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708f7ab48190b60a4bb8fdb17c8e |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e217369b648190914c58db6f6e0200 |
completed | April 17, 2026, 11:19 a.m. |
Created at: April 8, 2026, 9:22 p.m.