Triple
T29877701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ke Chieh |
E758786
|
entity |
| Predicate | romanizesNameOf |
P157446
|
FINISHED |
| Object | Ke Jie |
—
|
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: Ke Jie | Statement: [Ke Chieh, romanizesNameOf, Ke Jie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanizesNameOf Context triple: [Ke Chieh, romanizesNameOf, Ke Jie]
-
A.
romanizationFrom
Indicates that one entity is a romanized representation derived from the script or writing system of another entity.
-
B.
nameInLanguageRomanization
Indicates that an entity’s name is represented in the romanized (Latin-script) form of a particular language.
-
C.
romanizesVowel
Indicates the action of converting a vowel from a non-Roman writing system into its corresponding representation in the Roman (Latin) alphabet.
-
D.
romanizedUnder
Indicates that one written form is a romanized representation (using the Latin alphabet) of another form written in a different script.
-
E.
exampleRomanization
chosen
Indicates that one entity is a romanized representation (in Latin script) of the other entity’s original text or name.
- 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_69f2245d0d7081909e37ee328542bcd7 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6a0ea04888190ac3a813b603bcb5c |
completed | May 3, 2026, 1:12 a.m. |
| PD | Predicate disambiguation | batch_69f69fe463248190aa78128abeab1183 |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 29, 2026, 5:56 p.m.