Triple
T22820118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Hourn |
E565203
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Kim Hourn |
—
|
NE NERFINISHED |
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: Kim Hourn | Statement: [Kim Hourn, name, Kim Hourn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Hourn Context triple: [Kim Hourn, name, Kim Hourn]
-
A.
Kim Hourn
chosen
Kim Hourn is a Cambodian academic and diplomat best known for serving as Secretary-General of the Association of Southeast Asian Nations (ASEAN).
-
B.
Kinam Kim
Kinam Kim is a prominent South Korean semiconductor executive and technologist recognized for his leadership and contributions to the global chip industry.
-
C.
Mary Sohn
Mary Sohn is an American actress and comedian best known for her role on the NBC sitcom A.P. Bio.
-
D.
Karen Kim
Karen Kim is a central character in the TV drama "Mistresses," known for her complex personal life and morally ambiguous romantic entanglements.
-
E.
In-Kyung Kim
In-Kyung Kim is a South Korean professional golfer known for her multiple LPGA Tour victories and strong performances in international tournaments.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458426188190b58b8ab4844fe420 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17dcf39a88190bec26affc304236d |
completed | April 29, 2026, 3:41 a.m. |
Created at: April 17, 2026, 3:33 p.m.