Triple
T15085154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chahar |
E360252
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Zhangjiakou |
E144793
|
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: Zhangjiakou | Statement: [Chahar, capital, Zhangjiakou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhangjiakou Context triple: [Chahar, capital, Zhangjiakou]
-
A.
Zhangjiakou
chosen
Zhangjiakou is a major city in northern China known as a key gateway between Beijing and Inner Mongolia and as one of the host locations for the 2022 Winter Olympics.
-
B.
Chengde
Chengde is a historic city in northeastern China best known for its Qing dynasty Mountain Resort, a vast imperial summer retreat and UNESCO World Heritage Site.
-
C.
Baoding
Baoding is a historic prefecture-level city in central Hebei Province, China, known as a regional transportation hub and former military and administrative center.
-
D.
Langfang
Langfang is a prefecture-level city in northern China situated between Beijing and Tianjin, known for its strategic location and growing industrial and service sectors.
-
E.
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.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00275cfa88190a13fe20b585d9fcb |
completed | April 15, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feae19d0f0819089f330271c9fc6f7 |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 10, 2026, 3:03 a.m.