Triple
T224153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese |
E4278
|
entity |
| Predicate | hasPolitenessLevel |
P3329
|
FINISHED |
| Object | sonkeigo |
—
|
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: sonkeigo | Statement: [Japanese, hasPolitenessLevel, sonkeigo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolitenessLevel Context triple: [Japanese, hasPolitenessLevel, sonkeigo]
-
A.
politenessLevel
chosen
Indicates the degree of courteousness or respectfulness expressed by one entity toward another in an interaction.
-
B.
honorLevel
Indicates the degree or status of respect, distinction, or recognition accorded to an entity relative to others.
-
C.
hasLevel
Indicates that an entity possesses or is associated with a particular degree, rank, or stage within an ordered scale or hierarchy.
-
D.
formalityLevel
Indicates the degree of social or stylistic formality characterizing an interaction, expression, or context between entities.
-
E.
hasHonorificName
Indicates that an entity is referred to by a formal or respectful title or name used as an honorific.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dec53ac8190912f3d79576131fa |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b5739dc8190bad8bfa330ce0499 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.