Triple
T30513727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chungshan |
E776500
|
entity |
| Predicate | hasFormerTransliteration |
P65221
|
FINISHED |
| Object | Chung-shan |
—
|
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: Chung-shan | Statement: [Chungshan, hasFormerTransliteration, Chung-shan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormerTransliteration Context triple: [Chungshan, hasFormerTransliteration, Chung-shan]
-
A.
hasFormerRomanization
Indicates that an entity was previously written or represented using an earlier or superseded system of Romanized spelling.
-
B.
formerTransliteration
chosen
Indicates that one transliteration was previously used for an entity but has since been replaced by a different transliteration.
-
C.
hasTransliterationRole
Indicates that an entity participates in a transliteration process with a specific role (e.g., source, target, or agent of transliteration).
-
D.
hasTransliterationType
Indicates the type or system of transliteration used to convert text from one writing system into another.
-
E.
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.
- 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_69f2249a155c8190b1d512106007e9bb |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: April 29, 2026, 8:16 p.m.