Triple
T105544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanji |
E2128
|
entity |
| Predicate | numberOfCommonUseCharacters |
P5796
|
FINISHED |
| Object | about 2136 jōyō kanji |
—
|
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: about 2136 jōyō kanji | Statement: [Kanji, numberOfCommonUseCharacters, about 2136 jōyō kanji]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCommonUseCharacters Context triple: [Kanji, numberOfCommonUseCharacters, about 2136 jōyō kanji]
-
A.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
B.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
C.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
D.
hasCommonValue
Indicates that two or more entities share at least one identical value or attribute in common.
-
E.
usesDiacritics
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
- 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.