Triple
T35446983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 東洋のルソー |
E1024513
|
entity |
| Predicate | labelInRomaji |
P143799
|
FINISHED |
| Object | Tōyō no Rusō |
—
|
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: Tōyō no Rusō | Statement: [東洋のルソー, labelInRomaji, Tōyō no Rusō]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: labelInRomaji Context triple: [東洋のルソー, labelInRomaji, Tōyō no Rusō]
-
A.
nameInLanguageRomanization
Indicates that an entity’s name is represented in the romanized (Latin-script) form of a particular language.
-
B.
officialNameInRomaji
Indicates that an entity’s official name is written using the Roman alphabet (romaji) representation.
-
C.
exampleRomanization
Indicates that one entity is a romanized representation (in Latin script) of the other entity’s original text or name.
-
D.
JapaneseNameReading
chosen
Indicates that one entity is the reading or pronunciation (e.g., in kana or romaji) of a Japanese name represented by the other entity.
-
E.
romanizationType
Indicates the specific system or method used to convert text from one writing system into its Roman (Latin) alphabet representation.
- 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_69f76df8089481909f0018266ee881b7 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79ec355048190af30123ceb6efa2b |
completed | May 3, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:04 p.m.