Triple
T11309952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 大阪工業大学枚方キャンパス |
E267810
|
entity |
| Predicate | 言語環境 |
P19095
|
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: [大阪工業大学枚方キャンパス, 言語環境, 日本語]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 言語環境 Context triple: [大阪工業大学枚方キャンパス, 言語環境, 日本語]
-
A.
languageOfEnvironment
chosen
Indicates the language predominantly used or present in a given environment or context.
-
B.
sociolinguisticSituation
Indicates the social and cultural context in which language is used, including factors like participants, setting, norms, and power relations that shape linguistic behavior.
-
C.
languageShift
Indicates a change in the primary language used by an entity, such as switching from one language to another over time or in a given context.
-
D.
languageAdvocated
Indicates that an entity actively supports, promotes, or argues in favor of the use or adoption of a particular language.
-
E.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9c0b3b88190ac0e3d6a5ad3b9bc |
completed | April 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69d787aa31888190860eecaa80da5b20 |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:32 p.m.