Triple
T12660409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huangzhou |
E302407
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Huanggang |
E41595
|
NE 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: Huanggang | Statement: [Huangzhou, partOf, Huanggang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huanggang Context triple: [Huangzhou, partOf, Huanggang]
-
A.
Huanggang
chosen
Huanggang is a significant prefecture-level city in eastern Hubei, China, known for its long history, agricultural production, and proximity to the Yangtze River.
-
B.
Qianjiang
Qianjiang is a city in China known for its regional industry and cultural exchanges, including international town twinning partnerships.
-
C.
Sichun
Sichun is a Chinese given name notably borne by actress Ma Sichun, known for her roles in contemporary Chinese cinema and television.
-
D.
Fuyang
Fuyang is a major prefecture-level city in northwestern Anhui Province, China, known as a regional transportation and agricultural hub.
-
E.
Huangchu
Huangchu was the first era name of the Cao Wei state during China’s Three Kingdoms period, marking the early reign of Emperor Cao Pi.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c730b5c8190ae8dbb476e53729e |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 9, 2026, 5:19 p.m.