Triple
T17664400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mi River |
E440336
|
entity |
| Predicate | hasMouth |
P1008
|
FINISHED |
| Object | Xiang 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: Xiang River | Statement: [Mi River, hasMouth, Xiang River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xiang River Context triple: [Mi River, hasMouth, Xiang River]
-
A.
Xiang River
chosen
The Xiang River is a major waterway in southern China that flows through Hunan province and has historically been an important route for transport, culture, and military campaigns.
-
B.
Taizi River
The Taizi River is a major river in northeastern China that flows through Liaoning Province, including the city of Benxi, and serves as an important regional waterway.
-
C.
Luhan River
The Luhan River is a waterway in eastern Ukraine that flows through the Luhansk region, including the town of Slavyanoserbsk.
-
D.
Xiangxi River
The Xiangxi River is a tributary waterway in China that feeds into the Jialing River within the upper Yangtze River basin.
-
E.
Luo River
The Luo River is a significant tributary in central China that flows through Henan and Shaanxi provinces before joining the Yellow River.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46ea7f0ec81908eff43aa845584af |
completed | April 19, 2026, 5:56 a.m. |
Created at: April 10, 2026, 9:54 a.m.