Triple
T17595841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 秀喜 |
E428568
|
entity |
| Predicate | hasAlternativeScripts |
P57346
|
FINISHED |
| Object | may have hiragana form |
—
|
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: may have hiragana form | Statement: [秀喜, hasAlternativeScripts, may have hiragana form]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlternativeScripts Context triple: [秀喜, hasAlternativeScripts, may have hiragana form]
-
A.
hasAlternativeVocalization
Indicates that an entity has another valid way it can be vocalized or pronounced, distinct from its primary or standard vocalization.
-
B.
hasAlternativeNotation
chosen
Indicates that an entity can be represented or written in a different, equivalent form or notation.
-
C.
associatedLanguageScript
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
D.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
-
E.
hasDistinctLetterForms
Indicates that the related writing system or symbol set uses different visual shapes or styles for the same letter in different contexts (such as position, case, or usage).
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469ead59c8190a06519311891af3c |
completed | April 19, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fff0348190b899a32da537eaca |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:51 a.m.