Triple
T34115606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoko Nakamura |
E874961
|
entity |
| Predicate | hasNameComponentScript |
P63723
|
FINISHED |
| Object | 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: Kanji | Statement: [Yoko Nakamura, hasNameComponentScript, Kanji]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameComponentScript Context triple: [Yoko Nakamura, hasNameComponentScript, Kanji]
-
A.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
-
B.
hasUnicodeScriptName
Indicates that an entity is associated with a specific Unicode script name that characterizes the writing system it belongs to.
-
C.
hasNameInTeluguScript
Indicates that an entity is associated with a name written in the Telugu script.
-
D.
hasUnicodeName
Indicates that an entity is associated with a specific official Unicode name assigned to a character or symbol.
-
E.
nativeNameScript
chosen
Indicates the writing system or script in which an entity’s native name is expressed.
- 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_69f349a9271c81909576994c9ef7b179 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fef3ceef648190b58027c93d757438 |
completed | May 9, 2026, 8:43 a.m. |
| PD | Predicate disambiguation | batch_69fef359da2c819091a034387b08821f |
completed | May 9, 2026, 8:42 a.m. |
Created at: May 1, 2026, 1:53 a.m.