Triple
T11023713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jin Lee |
E260559
|
entity |
| Predicate | parentOf |
P120
|
FINISHED |
| Object | Meilin "Mei" Lee |
E257349
|
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: Meilin "Mei" Lee | Statement: [Jin Lee, parentOf, Meilin "Mei" Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meilin "Mei" Lee Context triple: [Jin Lee, parentOf, Meilin "Mei" Lee]
-
A.
Meilin "Mei" Lee
chosen
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.
-
B.
Amy Wong
Amy Wong is a wealthy, klutzy intern and engineering student from Mars who serves as one of the core characters in the animated sci-fi comedy series Futurama.
-
C.
Vivian Lee
Vivian Lee is a prominent architect and key leader at the internationally renowned firm Richard Meier & Partners Architects.
-
D.
Peng-Peng Lee
Peng-Peng Lee is a Canadian artistic gymnast and Olympic team member best known for her standout collegiate career with the UCLA Bruins, where she became a fan favorite for her exceptional beam and bars routines.
-
E.
Julia Wong
Julia Wong is a film editor best known for her work on major Hollywood productions, including the superhero movie "X-Men: The Last Stand."
- 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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797be9f148190a3a967bad5947496 |
completed | April 9, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d587dd08190ba466a4ffedde1e0 |
completed | April 19, 2026, 1:18 a.m. |
Created at: April 8, 2026, 9:25 p.m.