Triple
T20535061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Young |
E504171
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Young Kim
Young Kim is a South Korean-born American politician serving as a Republican member of the U.S. House of Representatives from California.
|
E1437848
|
NE FINISHED |
How this triple was built (4 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: Young Kim | Statement: [Kim Young, hasVariant, Young Kim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Young Kim Context triple: [Kim Young, hasVariant, Young Kim]
-
A.
Young Bae
Young Bae is a character portrayed by Canadian actor Dylan Schmid, likely in a film or television production.
-
B.
Jung Kim
Jung Kim is the charismatic and quick-witted convenience store manager and son in the Canadian sitcom "Kim's Convenience."
-
C.
Alex Kim
Alex Kim is an astrophysicist known for contributions to observational cosmology and dark energy research as part of the Supernova Cosmology Project.
-
D.
Marcus Kim
Marcus Kim is the romantic lead and childhood friend character portrayed by Randall Park in the Netflix romantic comedy film "Always Be My Maybe."
-
E.
Ki Jin Kim
Ki Jin Kim is a film producer best known for working on the critically acclaimed 2017 independent drama "Columbus."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Young Kim Triple: [Kim Young, hasVariant, Young Kim]
Generated description
Young Kim is a South Korean-born American politician serving as a Republican member of the U.S. House of Representatives from California.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Young Kim Target entity description: Young Kim is a South Korean-born American politician serving as a Republican member of the U.S. House of Representatives from California.
-
A.
Young Bae
Young Bae is a character portrayed by Canadian actor Dylan Schmid, likely in a film or television production.
-
B.
Jung Kim
Jung Kim is the charismatic and quick-witted convenience store manager and son in the Canadian sitcom "Kim's Convenience."
-
C.
Alex Kim
Alex Kim is an astrophysicist known for contributions to observational cosmology and dark energy research as part of the Supernova Cosmology Project.
-
D.
Marcus Kim
Marcus Kim is the romantic lead and childhood friend character portrayed by Randall Park in the Netflix romantic comedy film "Always Be My Maybe."
-
E.
Ki Jin Kim
Ki Jin Kim is a film producer best known for working on the critically acclaimed 2017 independent drama "Columbus."
- F. None of above. chosen
Provenance (5 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_69e0b4b476648190bc6019622ae54d3c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a06df04081908fa95c6214f06093 |
completed | April 20, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08a5638f688190b74c79fcb506a24d |
completed | May 16, 2026, 5:12 p.m. |
| NEDg | Description generation | batch_6a08a9459444819089c180ede20cc100 |
completed | May 16, 2026, 5:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08a9c8ae4c8190951fb838bd8c5e91 |
completed | May 16, 2026, 5:30 p.m. |
Created at: April 16, 2026, 11:37 a.m.