Triple
T19376930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | He Yifan |
E484695
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Yifan |
—
|
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: Yifan | Statement: [He Yifan, givenName, Yifan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yifan Context triple: [He Yifan, givenName, Yifan]
-
A.
Ziyu
Ziyu was the courtesy name of Zengzi, a prominent disciple of Confucius known for his filial piety and role in transmitting Confucian teachings.
-
B.
He Yifan
chosen
He Yifan is a Chinese individual notable enough to be recognized as a prominent bearer of the surname He.
-
C.
Yijun
Yijun is the given name of Zhu Yijun, better known as the Wanli Emperor of China's Ming dynasty.
-
D.
Jia-Ning
Jia-Ning is one of the central daughters in Ang Lee’s film "Eat Drink Man Woman," whose personal and romantic struggles reflect the evolving dynamics of her traditional Taiwanese family.
-
E.
Henry Zhou
Henry Zhou is a researcher in machine learning and speech processing, known for co-authoring work on the Wav2Vec2 self-supervised speech representation model.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a5cfbf48190ac60e3ffa6baa263 |
completed | April 20, 2026, 12:21 p.m. |
Created at: April 10, 2026, 1:35 p.m.