Triple
T31768869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 鄭 |
E810882
|
entity |
| Predicate | romanizedAsInMcCuneReischauer |
P78997
|
FINISHED |
| Object | Chŏng |
—
|
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: Chŏng | Statement: [鄭, romanizedAsInMcCuneReischauer, Chŏng]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanizedAsInMcCuneReischauer Context triple: [鄭, romanizedAsInMcCuneReischauer, Chŏng]
-
A.
romanizedUnder
Indicates that one written form is a romanized representation (using the Latin alphabet) of another form written in a different script.
-
B.
romanizationVariantOf
Indicates that one written form is a different romanized representation of the same underlying word or expression as another.
-
C.
nameInMcCuneReischauer
chosen
Indicates that an entity’s name is represented using the McCune–Reischauer romanization system.
-
D.
romanizationType
Indicates the specific system or method used to convert text from one writing system into its Roman (Latin) alphabet representation.
-
E.
romanizedCenter
Indicates that one entity is the central or primary romanized (Latin-script) representation associated with another entity.
- 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_69f348e463e08190b902d4819195e1f0 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 30, 2026, 11:33 p.m.