Triple
T21009670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fukuoka-Kitakyushu metropolitan area |
E517511
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Kama |
—
|
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: Kama | Statement: [Fukuoka-Kitakyushu metropolitan area, hasMajorCity, Kama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kama Context triple: [Fukuoka-Kitakyushu metropolitan area, hasMajorCity, Kama]
-
A.
Kama
chosen
Kama is a small city located in Japan’s Fukuoka Prefecture on the island of Kyushu.
-
B.
Kama
Kama is a surname of likely West African origin, notably borne by figures such as Senegalese jurist and former International Criminal Tribunal judge Laïty Kama.
-
C.
Kama
Kama is the Hindu god of love, desire, and attraction, often depicted as a youthful archer who inspires romantic and sensual longing.
-
D.
Kamari
Kamari is a popular seaside village on the southeast coast of Santorini, Greece, known for its black-sand beach and tourist-friendly promenade.
-
E.
Mataranka
Mataranka is a small town in Australia's Northern Territory, known for its thermal springs and location near Elsey National Park.
- 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_69e0b50192308190a284fcc89dd23a49 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc3edc548190987a6c2c9936286a |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:53 p.m.