Triple
T11023712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jin Lee |
E260559
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Meilin 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 Lee | Statement: [Jin Lee, child, Meilin Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meilin Lee Context triple: [Jin Lee, child, Meilin 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.
Chinsea Lee
Chinsea Lee is a Jamaican dancehall and reggae artist and songwriter, better known by her stage name Shenseea, recognized for her energetic performances and genre-blending hits.
-
C.
Lisa Ling
Lisa Ling is an American journalist, television presenter, and author known for her in-depth reporting and documentary work on social, cultural, and global issues.
-
D.
Vivian Lee
Vivian Lee is a prominent architect and key leader at the internationally renowned firm Richard Meier & Partners Architects.
-
E.
Jennifer Lien
Jennifer Lien is an American actress best known for her role as Kes on the television series "Star Trek: Voyager."
- 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_69e3e72b5e3881908e7230c03dc35f85 |
completed | April 18, 2026, 8:18 p.m. |
Created at: April 8, 2026, 9:25 p.m.