Triple
T426973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lei Yixin |
E9629
|
entity |
| Predicate | hasWorkLocation |
P1527
|
FINISHED |
| Object | Changsha |
E29743
|
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: Changsha | Statement: [Lei Yixin, hasWorkLocation, Changsha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Changsha Context triple: [Lei Yixin, hasWorkLocation, Changsha]
-
A.
Changsha
chosen
Changsha is the capital city of Hunan Province in south-central China, known as a historic cultural center and major regional economic hub.
-
B.
Xiangtan
Xiangtan is a prefecture-level city in central Hunan Province, China, known as an important industrial and commercial hub and for encompassing Shaoshan, the birthplace of Mao Zedong.
-
C.
Guangzhou
Guangzhou is a major port city in southern China and the capital of Guangdong Province, known as a key commercial and manufacturing hub in the Pearl River Delta.
-
D.
Wuhan
Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
-
E.
Huangshi
Huangshi is an industrial city in eastern Hubei Province, China, known for its steel production and location along the Yangtze River.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eed691c4819092b7e57306114bbc |
completed | Feb. 28, 2026, 1:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4fc783c948190bc61a51fc3e3ff1f |
completed | March 2, 2026, 2:56 a.m. |
Created at: Feb. 28, 2026, 1:11 p.m.