Triple
T14886572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jongno District |
E350135
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Hyehwa Station
Hyehwa Station is a Seoul Metropolitan Subway station serving the lively Daehangno theater and university district in central Seoul.
|
E1134201
|
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: Hyehwa Station | Statement: [Jongno District, contains, Hyehwa Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hyehwa Station Context triple: [Jongno District, contains, Hyehwa Station]
-
A.
Yeonsu Station
Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
-
B.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
C.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
D.
Gyeyang Station
Gyeyang Station is a major transit hub in Incheon, South Korea, serving as an interchange between the Incheon Subway, AREX airport railroad, and local bus routes.
-
E.
Sangmu Station
Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
- 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: Hyehwa Station Triple: [Jongno District, contains, Hyehwa Station]
Generated description
Hyehwa Station is a Seoul Metropolitan Subway station serving the lively Daehangno theater and university district in central Seoul.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hyehwa Station Target entity description: Hyehwa Station is a Seoul Metropolitan Subway station serving the lively Daehangno theater and university district in central Seoul.
-
A.
Yeonsu Station
Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
-
B.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
C.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
D.
Gyeyang Station
Gyeyang Station is a major transit hub in Incheon, South Korea, serving as an interchange between the Incheon Subway, AREX airport railroad, and local bus routes.
-
E.
Sangmu Station
Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5f5b1c88190815f3585770cb135 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dbf1f5c8190ad4626d14e0d8109 |
completed | May 9, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fea10834ac8190b4fbd0a165439110 |
completed | May 9, 2026, 2:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fea19745c08190b0efd33ee3f97fe1 |
completed | May 9, 2026, 2:53 a.m. |
Created at: April 10, 2026, 1:56 a.m.