Triple
T6978218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yantai |
E161767
|
entity |
| Predicate | hasCountyLevelCity |
P27799
|
FINISHED |
| Object | Zhaoyuan |
E449915
|
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: Zhaoyuan | Statement: [Yantai, hasCountyLevelCity, Zhaoyuan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhaoyuan Context triple: [Yantai, hasCountyLevelCity, Zhaoyuan]
-
A.
Zhaoyuan
chosen
Zhaoyuan is a county-level city in eastern China's Shandong province, known for its rich gold mining industry and economic development.
-
B.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
C.
Jianyang
Jianyang is a county-level city in northern Fujian Province, China, known for its historical role in tea production and its location along the Min River.
-
D.
Lingang
Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
-
E.
Lincang
Lincang is a prefecture-level city in southwestern China known for its tea production, diverse ethnic cultures, and location near the border with Myanmar.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db68d25c8190a1776908619ad979 |
completed | March 27, 2026, 7:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7754c817c8190a8b17a5e2c4f1b05 |
completed | March 28, 2026, 6:29 a.m. |
Created at: March 27, 2026, 2:31 p.m.