Triple
T14757495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deng Wenge |
E346767
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Wendi Deng |
E65995
|
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: Wendi Deng | Statement: [Deng Wenge, alsoKnownAs, Wendi Deng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wendi Deng Context triple: [Deng Wenge, alsoKnownAs, Wendi Deng]
-
A.
Wendi Deng
chosen
Wendi Deng is a Chinese-American businesswoman and film producer best known for her high-profile marriage to media mogul Rupert Murdoch and her influential connections in global media and politics.
-
B.
Wendy Xu
Wendy Xu is a comics artist and illustrator known for her work on fantasy and young adult graphic novels.
-
C.
Tina Chen
Tina Chen is a Taiwanese-American actress known for her film and television work from the late 1960s onward, often portraying complex Asian and Asian-American characters.
-
D.
Lindsay Wu
Lindsay Wu is a biomedical scientist known for his research on aging and metabolism, particularly in the field of sirtuins and NAD⁺ biology.
-
E.
Wanda Li
Wanda Li is an energetic, adventurous student in the Magic School Bus series known for her enthusiasm, competitiveness, and willingness to dive into Ms. Frizzle’s wild science field trips.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7ef0fd48190bd4a8af128ef274c |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0ced0e2c8190a13e3b43e4d35560 |
completed | May 8, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:30 a.m.