Triple
T552176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hebei |
E11863
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object | Shijiazhuang |
E79577
|
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: Shijiazhuang | Statement: [Hebei, hasCapital, Shijiazhuang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shijiazhuang Context triple: [Hebei, hasCapital, Shijiazhuang]
-
A.
Shijiazhuang
chosen
Shijiazhuang is the capital and largest city of Hebei Province in northern China, known as a major industrial and transportation hub.
-
B.
Cangzhou
Cangzhou is a prefecture-level city in eastern Hebei Province, China, known for its location near the Bohai Sea and its traditional martial arts heritage.
-
C.
Tianjin
Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
-
D.
Anyang
Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
-
E.
Taiyuan
Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
- 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_69a4932941d08190815efd422f0b4ca7 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a499047bd4819089ca8345f1b6e46c |
completed | March 1, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac82de53808190a105311397acfef6 |
completed | March 7, 2026, 7:56 p.m. |
Created at: March 1, 2026, 7:32 p.m.