Triple
T22715817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Tai |
E561728
|
entity |
| Predicate | outflow |
P967
|
FINISHED |
| Object | Huangpu River |
—
|
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: Huangpu River | Statement: [Lake Tai, outflow, Huangpu River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huangpu River Context triple: [Lake Tai, outflow, Huangpu River]
-
A.
Huangpu River
chosen
The Huangpu River is a significant waterway in eastern China that flows through the heart of Shanghai, dividing the city and serving as a vital shipping and cultural artery.
-
B.
Zhujiang
Zhujiang is the Chinese name for the Pearl River, a major river system in southern China that flows through Guangzhou into the South China Sea.
-
C.
Dongjiang
Dongjiang, also known as the Dong River, is a major river in southern China that serves as an important water source for cities including Hong Kong and Guangzhou.
-
D.
Shenzhen River
The Shenzhen River is a boundary river in southern China that forms part of the border between Hong Kong and the city of Shenzhen.
-
E.
Luhan River
The Luhan River is a waterway in eastern Ukraine that flows through the Luhansk region, including the town of Slavyanoserbsk.
- 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_69e2454fc984819088213b58ee87a002 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1790ca59881909064d49f331fb711 |
completed | April 29, 2026, 3:20 a.m. |
Created at: April 17, 2026, 3:19 p.m.