Triple
T224152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese |
E4278
|
entity |
| Predicate | hasPolitenessSystem |
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: [Japanese, hasPolitenessSystem, keigo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolitenessSystem Context triple: [Japanese, hasPolitenessSystem, keigo]
-
A.
politenessLevel
Indicates the degree of courteousness or respectfulness expressed by one entity toward another in an interaction.
-
B.
honorSystem
Indicates a relationship where compliance, access, or behavior is governed by trust in individuals to act honestly without direct enforcement or verification.
-
C.
honourSystem
Indicates a relationship where compliance, access, or behavior is governed by trust in individuals to act honestly without direct enforcement or verification.
-
D.
hasPolity
Indicates that one entity possesses, controls, or is associated with a particular political organization, governance structure, or state-like authority.
-
E.
supportsPolicy
Indicates that one entity endorses, backs, or is in favor of a particular policy or set of policies.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69a25dea93b48190863a3704b233aa03 |
completed | Feb. 28, 2026, 3:15 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.