Triple
T10846704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orion Lee |
E256029
|
entity |
| Predicate | portrayed |
P1668
|
FINISHED |
| Object | Jin Lee |
E260559
|
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: Jin Lee | Statement: [Orion Lee, portrayed, Jin Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jin Lee Context triple: [Orion Lee, portrayed, Jin Lee]
-
A.
Jin Lee
chosen
Jin Lee is the gentle, supportive father of protagonist Meilin "Mei" Lee in Pixar's animated film "Turning Red."
-
B.
Ki Hong Lee
Ki Hong Lee is a Korean-American actor best known for his role as Minho in the Maze Runner film series.
-
C.
Haan Lee
Haan Lee is one of the children of acclaimed Taiwanese-American film director Ang Lee.
-
D.
Hoon Lee
Hoon Lee is an American actor and voice actor known for roles in series like Banshee and for voicing characters in animated shows and video games.
-
E.
SangYup Lee
SangYup Lee is a prominent South Korean automobile designer known for leading Hyundai’s global design direction, including acclaimed models like the Ioniq 5.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75113bc188190ac78df0c51d95de6 |
completed | April 9, 2026, 7:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d6c077608190822d66b23866f5eb |
completed | April 18, 2026, 12:56 a.m. |
Created at: April 8, 2026, 9:20 p.m.