Triple
T17790084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hiroshi Satō |
E444134
|
entity |
| Predicate | isTransliteratedUsing |
P62529
|
FINISHED |
| Object | Latin alphabet |
—
|
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: Latin alphabet | Statement: [Hiroshi Satō, isTransliteratedUsing, Latin alphabet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isTransliteratedUsing Context triple: [Hiroshi Satō, isTransliteratedUsing, Latin alphabet]
-
A.
hasTransliterationRole
Indicates that an entity participates in a transliteration process with a specific role (e.g., source, target, or agent of transliteration).
-
B.
transliterationType
Indicates the specific system or method used to convert text from one writing system into another using corresponding characters.
-
C.
transliterationTarget
chosen
Indicates that one entity is the target script or form into which another entity is transliterated.
-
D.
transliterationLanguage
Indicates the language whose writing system is used as the target when converting text from one script to another.
-
E.
hasTransliterationType
Indicates the type or system of transliteration used to convert text from one writing system into 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4879688908190a5428b1fa7525f62 |
completed | April 19, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_69e3d8d8e538819084f1584426b41d5e |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:13 a.m.