Triple
T9144839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zengdu District |
E219425
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Suizhou City |
E42779
|
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: Suizhou City | Statement: [Zengdu District, partOf, Suizhou City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suizhou City Context triple: [Zengdu District, partOf, Suizhou City]
-
A.
Suizhou
chosen
Suizhou is a county-level city in northern Hubei Province, China, known for its historical sites and role as a regional transport and economic hub.
-
B.
Ezhou
Ezhou is a prefecture-level city in eastern Hubei Province, China, known for its location along the Yangtze River and its growing role as a regional transportation and industrial hub.
-
C.
Guangshui
Guangshui is a county-level city in central China's Hubei province, known for its historical sites and role as a regional transportation hub.
-
D.
Xiangyang
Xiangyang is a historic prefecture-level city in northern Hubei Province, China, known for its strategic location on the Han River and well-preserved ancient city walls.
-
E.
Zaoyang City
Zaoyang City is a county-level city in Hubei Province, China, known for its historical significance and agricultural production.
- 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_69ca83e121dc81909912bd66953081c5 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca9166d308190a742ae68371439ee |
completed | April 1, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eaa8135081909225810ee5355bc3 |
completed | April 5, 2026, 4:52 a.m. |
Created at: March 30, 2026, 7:19 p.m.