Triple
T9690118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akio |
E234514
|
entity |
| Predicate | typicalNameOrderInJapan |
P20511
|
FINISHED |
| Object | family name followed by given name |
—
|
LITERAL 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: family name followed by given name | Statement: [Akio, typicalNameOrderInJapan, family name followed by given name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNameOrderInJapan Context triple: [Akio, typicalNameOrderInJapan, family name followed by given name]
-
A.
nameOrderInJapan
chosen
Indicates that the person’s name is written or presented in the Japanese order, with the family name appearing before the given name.
-
B.
nameInJapaneseKana
Indicates that an entity’s name is written or represented using Japanese kana characters.
-
C.
hasNameInJapanese
Indicates that an entity is associated with a specific name expressed in the Japanese language.
-
D.
rankByCommonnessInJapan
Indicates how items are ordered based on how commonly they occur or are found in Japan.
-
E.
eraNameInJapanese
Indicates the Japanese-language name used for a specific historical or calendar era.
- F. None of above.
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_69ca84ca73208190957a900c8543bdcc |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d02b20881909d7c0d5d6aaafcb0 |
completed | April 1, 2026, 10:32 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b840f081909f66bf0b66d17d9b |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:17 p.m.