Triple
T22703230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyung Hee University |
E561377
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Cho Chi-hyung
Cho Chi-hyung was a South Korean educator and founder who established Kyung Hee University, contributing significantly to the country’s modern higher education system.
|
E1550126
|
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: Cho Chi-hyung | Statement: [Kyung Hee University, foundedBy, Cho Chi-hyung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cho Chi-hyung Context triple: [Kyung Hee University, foundedBy, Cho Chi-hyung]
-
A.
Cho Dong-hyun
Cho Dong-hyun is a South Korean former footballer and manager known for his contributions to Korean club and youth football.
-
B.
Kim Haeyong
Kim Haeyong is a cinematographer best known for his work on the animated feature film "The Lego Ninjago Movie."
-
C.
Lee Kyu-hyung
Lee Kyu-hyung is a South Korean actor known for his versatile performances in television dramas, films, and theater.
-
D.
Donghae
Donghae is a coastal city in South Korea known for its port facilities and maritime connections, including international ferry routes.
-
E.
Cho Kyuhyun
Cho Kyuhyun is a South Korean singer, musical theatre actor, and television personality best known as a main vocalist of the K-pop boy group Super Junior.
- 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: Cho Chi-hyung Triple: [Kyung Hee University, foundedBy, Cho Chi-hyung]
Generated description
Cho Chi-hyung was a South Korean educator and founder who established Kyung Hee University, contributing significantly to the country’s modern higher education system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cho Chi-hyung Target entity description: Cho Chi-hyung was a South Korean educator and founder who established Kyung Hee University, contributing significantly to the country’s modern higher education system.
-
A.
Cho Dong-hyun
Cho Dong-hyun is a South Korean former footballer and manager known for his contributions to Korean club and youth football.
-
B.
Kim Haeyong
Kim Haeyong is a cinematographer best known for his work on the animated feature film "The Lego Ninjago Movie."
-
C.
Lee Kyu-hyung
Lee Kyu-hyung is a South Korean actor known for his versatile performances in television dramas, films, and theater.
-
D.
Donghae
Donghae is a coastal city in South Korea known for its port facilities and maritime connections, including international ferry routes.
-
E.
Cho Kyuhyun
Cho Kyuhyun is a South Korean singer, musical theatre actor, and television personality best known as a main vocalist of the K-pop boy group Super Junior.
- 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_69e2454e615481909c177440be559d2c |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178cbf5788190bc8cd1bc71a861e5 |
completed | April 29, 2026, 3:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b7ed40b5c8190a2ac99d215de0c31 |
completed | May 18, 2026, 9:04 p.m. |
| NEDg | Description generation | batch_6a0b7f9ccb00819092fe5e9d71f545e4 |
completed | May 18, 2026, 9:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b806b4e1481909babc97a8914947a |
completed | May 18, 2026, 9:11 p.m. |
Created at: April 17, 2026, 3:16 p.m.