Triple
T5155025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kang Sae-byeok |
E116287
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Jung Ho-yeon |
E130249
|
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: Jung Ho-yeon | Statement: [Kang Sae-byeok, portrayedBy, Jung Ho-yeon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jung Ho-yeon Context triple: [Kang Sae-byeok, portrayedBy, Jung Ho-yeon]
-
A.
Jung Ho-yeon
chosen
Jung Ho-yeon is a South Korean model-turned-actress who gained international fame for her breakout role in the Netflix survival drama series "Squid Game."
-
B.
Jo Yun-ok
Jo Yun-ok is the wife of renowned Hong Kong martial artist, actor, and film director Sammo Hung.
-
C.
Jang Hye-jin
Jang Hye-jin is a South Korean actress best known internationally for her role as the resourceful housekeeper in the Academy Award–winning film "Parasite."
-
D.
Cha Jeong-in
Cha Jeong-in is a South Korean academic who serves as the president of Pusan National University.
-
E.
Kim Joo-ryoung
Kim Joo-ryoung is a South Korean actress best known internationally for her role in the hit Netflix survival drama 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_69bd445d94788190b72e2cc563120995 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd78ddb01081909457ff4208eac4d7 |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf3a86d58481909752c09fb9fec74e |
completed | March 22, 2026, 12:40 a.m. |
Created at: March 20, 2026, 1:44 p.m.