Triple
T19824917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kōgō Heika |
E476296
|
entity |
| Predicate | politenessCategory |
P9808
|
FINISHED |
| Object | keigo |
—
|
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: keigo | Statement: [Kōgō Heika, politenessCategory, keigo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: politenessCategory Context triple: [Kōgō Heika, politenessCategory, keigo]
-
A.
politenessLevel
Indicates the degree of courteousness or respectfulness expressed by one entity toward another in an interaction.
-
B.
hasPolitePronoun
Indicates that one entity refers to another using a polite or honorific form of address in language.
-
C.
hasPolitenessSystem
chosen
Indicates that a language or communication system includes formalized ways of expressing different levels of politeness or social hierarchy.
-
D.
decorum
Indicates adherence to socially accepted standards of proper, polite, or appropriate behavior in a given context.
-
E.
hasManner
Indicates the way, style, or method in which an action is performed or a relation is carried out.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e655017c188190ae9e17ae6b0eee05 |
completed | April 20, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69e5305bda388190a23b7191768107b1 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:50 p.m.