Triple
T20387424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Off Centre |
E497994
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Sung-Hi Lee
Sung-Hi Lee is a Korean-born American model and actress known for her work in magazines, films, and television.
|
E1433458
|
NE FINISHED |
How this triple was built (4 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: Sung-Hi Lee | Statement: [Off Centre, hasCastMember, Sung-Hi Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sung-Hi Lee Context triple: [Off Centre, hasCastMember, Sung-Hi Lee]
-
A.
Soo-Yung Han
Soo-Yung Han is the young daughter of a Chinese consul whose kidnapping repeatedly drives the central plot and emotional stakes of the Rush Hour film series.
-
B.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
-
C.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
D.
So-hee Kim
So-hee Kim is a television producer best known for her executive production work on the historical sci-fi drama series "Project Blue Book."
-
E.
Ji-Yoon Kim
Ji-Yoon Kim is the beleaguered yet determined new chair of a struggling university English department in the Netflix dramedy "The Chair," juggling academic politics, cultural change, and single motherhood.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sung-Hi Lee Triple: [Off Centre, hasCastMember, Sung-Hi Lee]
Generated description
Sung-Hi Lee is a Korean-born American model and actress known for her work in magazines, films, and television.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sung-Hi Lee Target entity description: Sung-Hi Lee is a Korean-born American model and actress known for her work in magazines, films, and television.
-
A.
Soo-Yung Han
Soo-Yung Han is the young daughter of a Chinese consul whose kidnapping repeatedly drives the central plot and emotional stakes of the Rush Hour film series.
-
B.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
-
C.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
D.
So-hee Kim
So-hee Kim is a television producer best known for her executive production work on the historical sci-fi drama series "Project Blue Book."
-
E.
Ji-Yoon Kim
Ji-Yoon Kim is the beleaguered yet determined new chair of a struggling university English department in the Netflix dramedy "The Chair," juggling academic politics, cultural change, and single motherhood.
- F. None of above. chosen
Provenance (5 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790c935881908f901d058e6a83a9 |
completed | April 20, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a088b0181a4819091030ed92f04d2dc |
completed | May 16, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_6a088f2c8ccc8190a1dc02db8e799355 |
completed | May 16, 2026, 3:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088f81fd0c8190a006db7e4fa9b440 |
completed | May 16, 2026, 3:38 p.m. |
Created at: April 16, 2026, 11:28 a.m.