Triple
T16681195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ai (Chinese surname) |
E405341
|
entity |
| Predicate | transliterationToJapaneseKana |
P9811
|
FINISHED |
| Object | アイ |
—
|
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: アイ | Statement: [Ai (Chinese surname), transliterationToJapaneseKana, アイ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transliterationToJapaneseKana Context triple: [Ai (Chinese surname), transliterationToJapaneseKana, アイ]
-
A.
translationOfJapaneseName
Indicates that one entity is a translation of a Japanese name given by the other entity.
-
B.
canBeWrittenAsKana
Indicates that something (typically text or a term) is able to be represented using Japanese kana characters.
-
C.
usesKatakanaFor
chosen
Indicates that one entity is written or represented using katakana script in relation to another entity.
-
D.
romanizationFrom
Indicates that one entity is a romanized representation derived from the script or writing system of another entity.
-
E.
typicalTransliterationFrom
Indicates that one string is the standard or most commonly used transliteration of another string from one writing system to another.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37d6f5cf481909e7628bbaa884e5a |
completed | April 18, 2026, 12:47 p.m. |
| PD | Predicate disambiguation | batch_69e319bc73908190a0e38bc926b31f10 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:19 a.m.