Triple
T14151295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yohei Kono |
E350687
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kono |
E33223
|
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: Kono | Statement: [Yohei Kono, familyName, Kono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kono Context triple: [Yohei Kono, familyName, Kono]
-
A.
Kono
chosen
Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
-
B.
Kono
Kono is a major Mande language spoken primarily in parts of West Africa, notably in Sierra Leone and neighboring regions.
-
C.
Kokonoe
Kokonoe is a small mountainous town in Japan known for its hot springs, scenic highlands, and suspension bridges.
-
D.
Konna
Konna is a town in central Mali that gained prominence as a strategic battleground during the 2013 conflict between Malian and Islamist forces.
-
E.
Konedobu
Konedobu is a suburb of Port Moresby in Papua New Guinea, known for housing many government offices and administrative facilities.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6124e23481909e5132a40a1d8624 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7e86820819099d6e3d3d4229f0d |
completed | May 7, 2026, 8:36 p.m. |
Created at: April 10, 2026, 12:57 a.m.