Triple
T34567667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 草加市 |
E887535
|
entity |
| Predicate | 公用語的地位 |
P30223
|
FINISHED |
| Object | 日本語 |
—
|
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: 日本語 | Statement: [草加市, 公用語的地位, 日本語]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 公用語的地位 Context triple: [草加市, 公用語的地位, 日本語]
-
A.
所在国の公用語
Indicates that a language is an official language of the country in which the subject entity is located.
-
B.
officialLanguageUse
chosen
Indicates that a particular language is formally designated and used by an authority (such as a government or institution) for official communication, documentation, or functions.
-
C.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
D.
isCommonInLinguisticCommunity
Indicates that something (such as a word, expression, or linguistic feature) is widely used or frequently occurs within a particular linguistic community.
-
E.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
- 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_69f349d1a5fc81908557a46875b2f157 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f72092057c81909f58fd28fb484ca7 |
completed | May 3, 2026, 10:16 a.m. |
| PD | Predicate disambiguation | batch_69f71cc8074c81909ae09bea2acf1a09 |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 2:02 a.m.