Triple
T5264425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hwang Jun-ho |
E118904
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Wi Ha-joon |
E134202
|
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: Wi Ha-joon | Statement: [Hwang Jun-ho, portrayedBy, Wi Ha-joon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wi Ha-joon Context triple: [Hwang Jun-ho, portrayedBy, Wi Ha-joon]
-
A.
Wi Ha-joon
chosen
Wi Ha-joon is a South Korean actor and model best known internationally for his breakout role in the hit Netflix survival drama series "Squid Game."
-
B.
Cho Yo-han
Cho Yo-han is the Korean birth name of John Cho, a Korean American actor best known for his roles in the "Harold & Kumar" films and the "Star Trek" reboot series.
-
C.
Ban Woo-hyun
Ban Woo-hyun is one of the children of former UN Secretary-General Ban Ki-moon and his wife Yoo Soon-taek.
-
D.
Oh Se-hoon
Oh Se-hoon is a South Korean politician best known for serving multiple terms as the mayor of Seoul.
-
E.
Koo In-hwoi
Koo In-hwoi was a South Korean entrepreneur who built one of the country’s leading chaebols, the LG Group, helping pioneer its modern electronics and chemical industries.
- 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_69bd446a42c88190b7ecbef006561d55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7bd4a9888190a79ef8e64c764f86 |
completed | March 20, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf4871c22c8190986f12c2783f315a |
completed | March 22, 2026, 1:40 a.m. |
Created at: March 20, 2026, 1:51 p.m.