Triple
T33193888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese Ambassador to the United Kingdom |
E849691
|
entity |
| Predicate | officeHolderShortTitleInChinese |
P91623
|
FINISHED |
| Object | 中国驻英国大使 |
—
|
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: 中国驻英国大使 | Statement: [Chinese Ambassador to the United Kingdom, officeHolderShortTitleInChinese, 中国驻英国大使]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHolderShortTitleInChinese Context triple: [Chinese Ambassador to the United Kingdom, officeHolderShortTitleInChinese, 中国驻英国大使]
-
A.
officeHolderTitleInChinese
chosen
Indicates the Chinese-language title or designation held by an office holder in a given position or role.
-
B.
officeHolderTitleInPinyin
Indicates the pinyin (romanized Chinese) form of the title held by an office holder.
-
C.
officeHolderTitleInJapanese
Indicates the official title or designation of an office holder as expressed in the Japanese language.
-
D.
officeHolderTitleInKorean
Indicates the official title or designation of an office holder as expressed in the Korean language.
-
E.
officeHoldersTitleInRevisedRomanization
Indicates the official title held by an officeholder, expressed using the Revised Romanization system for Korean.
- 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_69f3495e0f108190a6a7006f79f9c2c3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff59b33a38819086cc9aa19b81748b |
completed | May 9, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69ff587758f88190a39c2164341dc554 |
completed | May 9, 2026, 3:53 p.m. |
Created at: May 1, 2026, 1:29 a.m.