Triple
T22082499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sawndip |
E545685
|
entity |
| Predicate | characterFormationMethod |
P146897
|
FINISHED |
| Object | phonetic borrowing from Chinese characters |
—
|
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: phonetic borrowing from Chinese characters | Statement: [Sawndip, characterFormationMethod, phonetic borrowing from Chinese characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterFormationMethod Context triple: [Sawndip, characterFormationMethod, phonetic borrowing from Chinese characters]
-
A.
characterForm
Indicates that one entity is a particular form, version, or transformation state of a character.
-
B.
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).
-
C.
isWrittenWith
Indicates that something is created or expressed using a particular writing tool, medium, or system.
-
D.
iconographicForm
Indicates the specific visual or symbolic representation that an entity takes within an image or artwork.
-
E.
mannerOfWriting
Indicates the way or style in which something is written or expressed in writing.
- F. None of above. chosen
Provenance (4 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_69e11e3523488190badd54b5d580c00d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128b706288190945c0c37e5ff2756 |
completed | April 28, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69e71b20ec50819096ac196c798f8e3c |
completed | April 21, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e7222d208c819098b12c13e31af629 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 8:28 p.m.