Triple
T3738935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Didi Chuxing |
E79652
|
entity |
| Predicate | keyPerson |
P256
|
FINISHED |
| Object | Jean Liu |
E387414
|
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: Jean Liu | Statement: [Didi Chuxing, keyPerson, Jean Liu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Liu Context triple: [Didi Chuxing, keyPerson, Jean Liu]
-
A.
Jean Liu
chosen
Jean Liu is a prominent Chinese business executive and technology entrepreneur best known for her leadership role in ride-hailing giant Didi Chuxing.
-
B.
Eugenia Yuan
Eugenia Yuan is a Hong Kong–born American actress and former rhythmic gymnast known for her roles in international martial arts and drama films.
-
C.
Jennifer Lien
Jennifer Lien is an American actress best known for her role as Kes on the television series "Star Trek: Voyager."
-
D.
Lori Huang
Lori Huang is the wife of NVIDIA co-founder and CEO Jensen Huang and is known for her low public profile despite her connection to the prominent tech executive.
-
E.
Meilin "Mei" Lee
Meilin "Mei" Lee is the energetic 13-year-old Chinese-Canadian girl in Pixar's "Turning Red" who transforms into a giant red panda whenever her emotions become overwhelming.
- 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_69ad8b115610819095b02007da5ca3cb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb404b908190b6b4ee583dee3cc9 |
completed | March 8, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb0c116c8190a74fff15a5de8296 |
completed | March 14, 2026, 6:07 a.m. |
Created at: March 8, 2026, 3:34 p.m.