Triple
T29689203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 冯 |
E751169
|
entity |
| Predicate | transliterationInPinyin |
P41219
|
FINISHED |
| Object | Feng |
—
|
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: Feng | Statement: [冯, transliterationInPinyin, Feng]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transliterationInPinyin Context triple: [冯, transliterationInPinyin, Feng]
-
A.
ChinesePinyin
chosen
Indicates that one entity is the Chinese pinyin (romanized phonetic transcription) representation of another entity.
-
B.
transliterationInVietnameseSinoVietnamese
Indicates that one term is the Vietnamese Sino-Vietnamese transliteration or reading of another term.
-
C.
transliterationTarget
Indicates that one entity is the target script or form into which another entity is transliterated.
-
D.
transliterationName
Indicates that one entity is the transliterated form of another entity’s name from one writing system into another.
-
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.
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_69f0d625b09481909b0b69aea1e846c8 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fd7fdafbe881908a31fcb407af2c34 |
completed | May 8, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69fd7ef0ea908190b5d83f71565bdb1c |
completed | May 8, 2026, 6:13 a.m. |
Created at: April 28, 2026, 7:15 p.m.