Triple
T105543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanji |
E2128
|
entity |
| Predicate | kun’yomiDerivedFrom |
P5795
|
FINISHED |
| Object | native Japanese words |
—
|
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: native Japanese words | Statement: [Kanji, kun’yomiDerivedFrom, native Japanese words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: kun’yomiDerivedFrom Context triple: [Kanji, kun’yomiDerivedFrom, native Japanese words]
-
A.
etymologyType
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
B.
hasRomanizationOf
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
C.
etymologicalLanguage
Indicates the language from which a word or term is historically derived in its etymology.
-
D.
etymology
Indicates the historical origin and development of a word or term, including its source language and form.
-
E.
hasEndonym
Indicates that an entity has a name or designation used by native speakers or within its own local language or community.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25711f6788190a22252ea3a3af394 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563be81c81908ccc5ed44edd6b8e |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2570f45bc81909ebba7ee5f602976 |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.