Triple
T10939745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kagawa Prefecture |
E258435
|
entity |
| Predicate | hasIsland |
P970
|
FINISHED |
| Object | Ogijima |
E416059
|
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: Ogijima | Statement: [Kagawa Prefecture, hasIsland, Ogijima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ogijima Context triple: [Kagawa Prefecture, hasIsland, Ogijima]
-
A.
Ogijima
chosen
Ogijima is a small inhabited island in Japan’s Seto Inland Sea, known for its traditional fishing village, scenic lighthouse, and contemporary art installations featured in the Setouchi Triennale.
-
B.
Kitaiōjima
Kitaiōjima is the former name of Kita Iōtō, a small volcanic island in the Ogasawara Islands of Japan known for its remote location and military history.
-
C.
Unoshima
Unoshima is a small island located within Lake Kawaguchi, a scenic lake near Mount Fuji in Japan.
-
D.
Amakusa
Amakusa is a group of islands and a city in Kumamoto Prefecture, Japan, known for its coastal scenery, historical Christian heritage, and fishing communities.
-
E.
Takadanobaba
Takadanobaba is a lively Tokyo neighborhood known for its student population, affordable eateries, and strong connections to nearby universities like Waseda.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770c1389881909341170984211810 |
completed | April 9, 2026, 9:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d722972c8190b14637dc9e52ce11 |
completed | April 18, 2026, 12:58 a.m. |
Created at: April 8, 2026, 9:23 p.m.