Triple
T8094574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guiyang |
E188949
|
entity |
| Predicate | nearRiver |
P350
|
FINISHED |
| Object | Wu River |
E99764
|
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: Wu River | Statement: [Guiyang, nearRiver, Wu River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wu River Context triple: [Guiyang, nearRiver, Wu River]
-
A.
Wu River
chosen
The Wu River is a significant river in southwestern China known for flowing through deep gorges and contributing substantially to the Yangtze River system.
-
B.
Jialing River
The Jialing River is a significant river in southwestern China that flows through Sichuan and Chongqing, contributing heavily to the region’s water resources, transportation, and ecology.
-
C.
Hunjiang River
The Hunjiang River is a significant river in northeastern China that serves as a major tributary within the Yalu River basin.
-
D.
Luo River
The Luo River is a significant tributary in central China that flows through Henan and Shaanxi provinces before joining the Yellow River.
-
E.
Jingjiang River
Jingjiang River is a historically significant, highly sinuous section of the Yangtze River in central China, known for its sharp bends and extensive river-training works.
- 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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb429089cc81909e4625f9cc7e305f |
completed | March 31, 2026, 3:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6e5358888190ad1b5771ca00a097 |
completed | April 3, 2026, 7:37 a.m. |
Created at: March 30, 2026, 5:30 p.m.