Triple
T8949560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shíyàn |
E213309
|
entity |
| Predicate | hasChineseName |
P4878
|
FINISHED |
| Object | 十堰 |
E191607
|
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: 十堰 | Statement: [Shíyàn, hasChineseName, 十堰]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 十堰 Context triple: [Shíyàn, hasChineseName, 十堰]
-
A.
十堰
chosen
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.
张湾区
张湾区是湖北省十堰市下辖的一个市辖区和主要城区之一，以工业基础和城市综合功能较为发达而著称。
-
C.
十堰市人民政府
十堰市人民政府 is the municipal government authority responsible for administering and managing public affairs, economic development, and social services in Shiyan City, Hubei Province, China.
-
D.
Hanzhong
Hanzhong is a historic prefecture-level city in southwestern Shaanxi, China, known as a key gateway between northern and southern China and for its rich cultural and natural landscapes.
-
E.
Jingmen
Jingmen is a prefecture-level city in central China known for its role as a regional industrial and transportation hub within Hubei Province.
- 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_69ca839843408190a39069a029a89f15 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc670b5f50819080f1c73992fe5281 |
completed | April 1, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc206550c8190abf016f25b14fa64 |
completed | April 3, 2026, 1:35 p.m. |
Created at: March 30, 2026, 6:59 p.m.