Triple
T7724494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ma Dong-seok |
E175095
|
entity |
| Predicate | nativeName |
P15
|
FINISHED |
| Object | 마동석 |
E684472
|
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: 마동석 | Statement: [Ma Dong-seok, nativeName, 마동석]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 마동석 Context triple: [Ma Dong-seok, nativeName, 마동석]
-
A.
Ma Dong-seok
chosen
Ma Dong-seok is a South Korean-American actor best known internationally for his tough-guy roles in films like "Train to Busan" and "The Outlaws."
-
B.
Kim Swoo Geun
Kim Swoo Geun was a prominent South Korean architect renowned for pioneering modern Korean architecture and shaping Seoul’s urban landscape in the mid-20th century.
-
C.
Lee Byung-chul
Lee Byung-chul was a South Korean entrepreneur and industrialist best known as the founder of the Samsung business empire, which grew into one of the world’s largest conglomerates.
-
D.
Lee Dong-hwi
Lee Dong-hwi is a South Korean actor known for his roles in popular films and television dramas, including the hit series "Reply 1988."
-
E.
I Jeong-jae
I Jeong-jae is the Korean romanization of the name of Lee Jung-jae, a prominent South Korean actor known internationally for his role in the series "Squid Game."
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7031279708190a3a5fb64f9206974 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be2de08881909715d9164b743aae |
completed | March 29, 2026, 5:52 a.m. |
Created at: March 27, 2026, 4:05 p.m.