Triple
T20002851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeongneung (Paju) |
E494377
|
entity |
| Predicate | KoreanName |
P17869
|
FINISHED |
| Object |
영릉
영릉은 조선 제21대 임금 영조와 정성왕후의 능이 있는 경기도 파주시의 조선 왕릉이다.
|
E1411645
|
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: 영릉 | Statement: [Yeongneung (Paju), KoreanName, 영릉]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 영릉 Context triple: [Yeongneung (Paju), KoreanName, 영릉]
-
A.
Mungyeong
Mungyeong is a city in South Korea known for its historic mountain passes, scenic hiking trails, and traditional cultural heritage.
-
B.
Wonju
Wonju is a city in South Korea’s Gangwon Province known historically as a strategic military site and today as a regional commercial and transportation hub.
-
C.
Jonggol
Jonggol is a rapidly developing district in West Java, Indonesia, known for its rural landscapes, growing residential areas, and proximity to the Jakarta metropolitan region.
-
D.
Sangju
Sangju is a city in southeastern South Korea known historically for agriculture, particularly rice and dried persimmons, and for its role as a regional transport hub.
-
E.
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
- 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: 영릉 Triple: [Yeongneung (Paju), KoreanName, 영릉]
Generated description
영릉은 조선 제21대 임금 영조와 정성왕후의 능이 있는 경기도 파주시의 조선 왕릉이다.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 영릉 Target entity description: 영릉은 조선 제21대 임금 영조와 정성왕후의 능이 있는 경기도 파주시의 조선 왕릉이다.
-
A.
Mungyeong
Mungyeong is a city in South Korea known for its historic mountain passes, scenic hiking trails, and traditional cultural heritage.
-
B.
Wonju
Wonju is a city in South Korea’s Gangwon Province known historically as a strategic military site and today as a regional commercial and transportation hub.
-
C.
Jonggol
Jonggol is a rapidly developing district in West Java, Indonesia, known for its rural landscapes, growing residential areas, and proximity to the Jakarta metropolitan region.
-
D.
Sangju
Sangju is a city in southeastern South Korea known historically for agriculture, particularly rice and dried persimmons, and for its role as a regional transport hub.
-
E.
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
- 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a2e34481908a495cc5d077c41f |
completed | April 20, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0826f82f848190b16286d3966d204c |
completed | May 16, 2026, 8:12 a.m. |
| NEDg | Description generation | batch_6a0828995470819097ea2998bb2d7be6 |
completed | May 16, 2026, 8:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0829ee7e5c819087701ea556f29974 |
completed | May 16, 2026, 8:25 a.m. |
Created at: April 11, 2026, 3:33 p.m.