Triple
T36660639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shōji |
E905112
|
entity |
| Predicate | hasKanjiVariation |
P95669
|
FINISHED |
| Object | multiple possible kanji combinations |
—
|
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: multiple possible kanji combinations | Statement: [Shōji, hasKanjiVariation, multiple possible kanji combinations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKanjiVariation Context triple: [Shōji, hasKanjiVariation, multiple possible kanji combinations]
-
A.
usesHanjaVariants
chosen
Indicates that one entity employs or incorporates alternative Hanja (Chinese character) forms corresponding to another entity.
-
B.
hasKanjiReading
Indicates that a written kanji character is associated with a specific reading or pronunciation.
-
C.
hasVariantWithoutToneMarks
Indicates that one textual form is a variant of another in which all tone marks have been removed.
-
D.
hasNameInKanji
Indicates that an entity is associated with a specific written form of its name in Kanji characters.
-
E.
usesKanjiFrom
Indicates that one writing system, word, or text incorporates or is composed of kanji characters originating from another specified source.
- 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_69f76e6e3b908190970251b30f76ad71 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c77c19948190a856ebf393846c98 |
completed | May 3, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69f7c4796ebc819084a0dc08505e5f14 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.