Triple
T4563076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jang Deok-su |
E121841
|
entity |
| Predicate | hasEnemy |
P4675
|
FINISHED |
| Object | Kang Sae-byeok |
E116287
|
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: Kang Sae-byeok | Statement: [Jang Deok-su, hasEnemy, Kang Sae-byeok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kang Sae-byeok Context triple: [Jang Deok-su, hasEnemy, Kang Sae-byeok]
-
A.
Kang Sae-byeok
chosen
Kang Sae-byeok is a North Korean defector and pickpocket who becomes one of the central, emotionally resonant contestants in the deadly survival competition of the South Korean series "Squid Game."
-
B.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
C.
Kwak Sin-ae
Kwak Sin-ae is a South Korean film producer best known for producing the Academy Award–winning film "Parasite."
-
D.
Heo Jeong
Heo Jeong was a South Korean politician who served as prime minister and played a significant role in the country’s early post-war democratic politics.
-
E.
Cha Jeong-in
Cha Jeong-in is a South Korean academic who serves as the president of Pusan National University.
- 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd5830b4308190a63699bf28922375 |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd3aa3b9081908984777207f4040e |
completed | March 20, 2026, 11:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.