Triple
T33330040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Kishida Cabinet |
E853376
|
entity |
| Predicate | hasCabinetNameInJapanese |
P9882
|
FINISHED |
| Object | 第1次岸田内閣 |
—
|
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: 第1次岸田内閣 | Statement: [First Kishida Cabinet, hasCabinetNameInJapanese, 第1次岸田内閣]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabinetNameInJapanese Context triple: [First Kishida Cabinet, hasCabinetNameInJapanese, 第1次岸田内閣]
-
A.
hasNameInJapanese
Indicates that an entity is associated with a specific name expressed in the Japanese language.
-
B.
hasOfficialNameInJapanese
chosen
Indicates that an entity has an official, formally recognized name expressed in the Japanese language.
-
C.
hasNameInKanji
Indicates that an entity is associated with a specific written form of its name in Kanji characters.
-
D.
nameInJapaneseKana
Indicates that an entity’s name is written or represented using Japanese kana characters.
-
E.
hasAlleyNameInJapanese
Indicates that an alley has a specific name expressed in the Japanese language.
- 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_69f34969614c81909cd99661b0902533 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff1f3f94fc819095955299f50ab4ce |
completed | May 9, 2026, 11:49 a.m. |
| PD | Predicate disambiguation | batch_69ff1ea47748819082f63d9b9d9c3e65 |
completed | May 9, 2026, 11:46 a.m. |
Created at: May 1, 2026, 1:34 a.m.