Triple
T24500706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenpō Kinenbi |
E617923
|
entity |
| Predicate | JapaneseScriptName |
P155698
|
FINISHED |
| Object | 憲法記念日 |
—
|
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: 憲法記念日 | Statement: [Kenpō Kinenbi, JapaneseScriptName, 憲法記念日]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JapaneseScriptName Context triple: [Kenpō Kinenbi, JapaneseScriptName, 憲法記念日]
-
A.
JapaneseNameReading
Indicates that one entity is the reading or pronunciation (e.g., in kana or romaji) of a Japanese name represented by the other entity.
-
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.
termInJapaneseScript
chosen
Indicates that a given term is written or represented using Japanese script (such as kanji, hiragana, or katakana).
-
E.
hasNameInKanji
Indicates that an entity is associated with a specific written form of its name in Kanji characters.
- 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_69e2d7f682108190a1a7ca5fd485ee8a |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:23 a.m.