Triple
T35737776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kai Province |
E1032939
|
entity |
| Predicate | ISOTransliteration |
P44932
|
FINISHED |
| Object | Kai no Kuni |
—
|
NE NERFINISHED |
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: Kai no Kuni | Statement: [Kai Province, ISOTransliteration, Kai no Kuni]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ISOTransliteration Context triple: [Kai Province, ISOTransliteration, Kai no Kuni]
-
A.
commonTransliterationSystem
Indicates that two or more written forms are derived using the same standardized system for converting text from one script to another.
-
B.
standardTransliteration
chosen
Indicates that one representation of text is a transliteration of another according to a recognized standard or convention.
-
C.
alternativeTransliteration
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
-
D.
transliterationLanguage
Indicates the language whose writing system is used as the target when converting text from one script to another.
-
E.
typicalTransliterationFrom
Indicates that one string is the standard or most commonly used transliteration of another string from one writing system to another.
- 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_69f76e10e59081908d81ad9ce22f40b6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:05 p.m.