Triple
T7822454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kibushi |
E181163
|
entity |
| Predicate | coexistsWith |
P1867
|
FINISHED |
| Object | Shimaore |
E287231
|
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: Shimaore | Statement: [Kibushi, coexistsWith, Shimaore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shimaore Context triple: [Kibushi, coexistsWith, Shimaore]
-
A.
Shimaore
chosen
Shimaore is a Bantu language closely related to Comorian, widely spoken by the local population of Mayotte in the Indian Ocean.
-
B.
Omishima
Omishima is a scenic island in Japan’s Seto Inland Sea, known for its cycling route on the Shimanami Kaido, historic Oyamazumi Shrine, and coastal landscapes.
-
C.
Kaminoshima
Kaminoshima is one of the uninhabited, forested islets of the Tomogashima archipelago in Wakayama Prefecture, Japan, known for its coastal scenery and wartime ruins.
-
D.
Yoroshima
Yoroshima is a small island that is part of Japan’s subtropical Amami archipelago in Kagoshima Prefecture.
-
E.
Takashima
Takashima is a lakeside city in western Shiga Prefecture, Japan, known for its scenic location along Lake Biwa and surrounding mountains.
- 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_69ca828153f48190bdb27ac46f8e0745 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cafa095d7081908b3e492ce58b5d5f |
completed | March 30, 2026, 10:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbd8698a88190b5f9b4d232504f04 |
completed | April 1, 2026, 6:39 a.m. |
Created at: March 30, 2026, 4:41 p.m.