Triple
T15803379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lai |
E383149
|
entity |
| Predicate | hasTransliterationSource |
P79218
|
FINISHED |
| Object | Mandarin Chinese |
—
|
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: Mandarin Chinese | Statement: [Lai, hasTransliterationSource, Mandarin Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransliterationSource Context triple: [Lai, hasTransliterationSource, Mandarin Chinese]
-
A.
hasTransliterationRole
chosen
Indicates that an entity participates in a transliteration process with a specific role (e.g., source, target, or agent of transliteration).
-
B.
hasTransliterationType
Indicates the type or system of transliteration used to convert text from one writing system into another.
-
C.
hasTransliterationRule
Indicates that there exists a specific rule or mapping that defines how text in one script or writing system is systematically converted into another.
-
D.
transliterationTarget
Indicates that one entity is the target script or form into which another entity is transliterated.
-
E.
hasTranslation
Indicates that one entity is a translation or translated version of another entity in a different language.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b524835c8190ae286b2562f07756 |
completed | April 16, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69e0053b847c8190945726c3ddac21cc |
completed | April 15, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:48 a.m.