Triple
T15623997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liu Cixin |
E375633
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Yangquan |
E341834
|
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: Yangquan | Statement: [Liu Cixin, birthPlace, Yangquan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yangquan Context triple: [Liu Cixin, birthPlace, Yangquan]
-
A.
Yangquan
chosen
Yangquan is an industrial city in northern China known for its coal mining and heavy industry within Shanxi Province.
-
B.
Datong
Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
-
C.
Wu’an
Wu’an is a county-level city administered by Handan in Hebei Province, northern China, known for its industrial development and coal resources.
-
D.
Fenyang
Fenyang is a county-level city in Shanxi Province, China, known for its historical heritage and role in regional commerce and culture.
-
E.
Yingtan
Yingtan is a prefecture-level city in eastern Jiangxi Province, China, known as a regional transport hub and for its proximity to the scenic Mount Longhu.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9cfd94819091459aa17a002eaf |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f3f65dc8190ac94db1d4d53d77f |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.