Triple
T16919518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bazhou North railway station |
E410407
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Bazhou city |
E1246793
|
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: Bazhou city | Statement: [Bazhou North railway station, serves, Bazhou city]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bazhou city Context triple: [Bazhou North railway station, serves, Bazhou city]
-
A.
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.
-
B.
Bozhou
Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
-
C.
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.
-
D.
Bazhou
chosen
Bazhou is a county-level city in Hebei Province, China, known as a regional transport hub within the Beijing–Tianjin–Hebei metropolitan area.
-
E.
Hengshui
Hengshui is a prefecture-level city in southeastern Hebei Province, China, known for its traditional culture, agriculture, and growing industrial base.
- 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cded2f8481909a20cc08b47e922e |
completed | April 18, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ecafd908190b8a1513138a29303 |
completed | May 11, 2026, 1:20 a.m. |
Created at: April 10, 2026, 5:30 a.m.