Triple
T20038524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Li Xiannian |
E497345
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Hubei |
—
|
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: Hubei | Statement: [Li Xiannian, region, Hubei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hubei Context triple: [Li Xiannian, region, Hubei]
-
A.
Hubei Province
chosen
Hubei Province is a landlocked region in central China known for its capital city Wuhan, major role in industry and transportation, and significant historical and cultural heritage.
-
B.
Hebei
Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
-
C.
Kansu
Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
-
D.
Zhili province
Zhili province was a historically important administrative region in northern China, centered on present-day Hebei and Beijing, that played a key political and military role during the late Qing and early Republican eras.
-
E.
Jiangsu
Jiangsu is a populous and economically significant coastal province in eastern China, known for its rich history, dense urbanization, and major cities such as Nanjing and Suzhou.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e9e99c81909b7d50eac893c414 |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:36 p.m.