Triple
T8202337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 十堰 |
E191607
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | 丹江口市 |
E192640
|
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: 丹江口市 | Statement: [十堰, hasSubdivision, 丹江口市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 丹江口市 Context triple: [十堰, hasSubdivision, 丹江口市]
-
A.
十堰
十堰 is a prefecture-level city in northwestern Hubei Province, China, known as an important automotive manufacturing base and gateway to the Wudang Mountains.
-
B.
黄冈市
黄冈市是中国湖北省东部的一座地级市，位于长江中游、与武汉相邻，以其红色革命历史和丰富的自然资源而闻名。
-
C.
汉阳
汉阳是中国湖北省武汉市的一个历史悠久的城区，位于长江与汉江交汇处，以其工业基础和文化遗产而闻名。
-
D.
Danjiangkou
chosen
Danjiangkou is a county-level city in northwestern Hubei Province, China, known for the Danjiangkou Reservoir, a key source for the South–North Water Transfer Project.
-
E.
Suizhou
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.
- 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_69ca82c7f3e08190857bf1fc63b2a10c |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb5df84b108190b4407a72a3500af9 |
completed | March 31, 2026, 5:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccedc49ba4819099762f200c4e6577 |
completed | April 1, 2026, 10:04 a.m. |
Created at: March 30, 2026, 5:43 p.m.