Triple
T10896556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pang |
E257324
|
entity |
| Predicate | hasTransliterationType |
P96284
|
FINISHED |
| Object | variant romanization |
—
|
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: variant romanization | Statement: [Pang, hasTransliterationType, variant romanization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransliterationType Context triple: [Pang, hasTransliterationType, variant romanization]
-
A.
hasTransliterationRole
Indicates that an entity participates in a transliteration process with a specific role (e.g., source, target, or agent of transliteration).
-
B.
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.
-
C.
transliterationName
Indicates that one entity is the transliterated form of another entity’s name from one writing system into another.
-
D.
hasTitleInTransliteration
Indicates that an entity has a specific title represented in a transliterated form from another writing system.
-
E.
transliterationLanguage
Indicates the language whose writing system is used as the target when converting text from one script to another.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75d02e4c88190b8286078e90bf913 |
completed | April 9, 2026, 8:02 a.m. |
| PD | Predicate disambiguation | batch_69d70d3943c881908895397eccc3e415 |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101de31c819090707635f6790559 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:21 p.m.