Triple
T23020436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiangmei Chen |
E573148
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Xiangmei Chen |
—
|
NE NERFINISHED |
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: Xiangmei Chen | Statement: [Xiangmei Chen, name, Xiangmei Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xiangmei Chen Context triple: [Xiangmei Chen, name, Xiangmei Chen]
-
A.
Xiangmei Chen
chosen
Xiangmei Chen, better known as Anna Chennault, was a prominent Chinese-American journalist, Republican political operative, and influential figure in U.S.–China relations during the Cold War.
-
B.
Mingda Chen
Mingda Chen is a researcher in natural language processing known for work on large-scale language models and representation learning, including contributions to the ALBERT model.
-
C.
Xue Chen
Xue Chen is a prominent Chinese beach volleyball player who has represented China in multiple international competitions, including the Olympic Games.
-
D.
Yuxin Peng
Yuxin Peng is a computer vision and machine learning researcher known for coauthoring influential papers alongside leading figures such as Shaoqing Ren.
-
E.
Danqi Chen
Danqi Chen is a prominent computer scientist and natural language processing researcher known for her work on neural reading comprehension and information retrieval.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b821008190b0e09cb02092aae1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e8324c81908b8868d298af66e1 |
completed | April 29, 2026, 4:07 a.m. |
Created at: April 17, 2026, 3:52 p.m.