Triple
T7026911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwangju Metro |
E162970
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Sangmu Station
Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
|
E650261
|
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: Sangmu Station | Statement: [Gwangju Metro, hasStation, Sangmu Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sangmu Station Context triple: [Gwangju Metro, hasStation, Sangmu Station]
-
A.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
B.
Yeonsu Station
Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
-
C.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
D.
Sasang Station
Sasang Station is a major railway and subway interchange in Busan, South Korea, serving as a key transit hub for both local and intercity travel.
-
E.
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.
- 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: Sangmu Station Triple: [Gwangju Metro, hasStation, Sangmu Station]
Generated description
Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sangmu Station Target entity description: Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
-
A.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
B.
Yeonsu Station
Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
-
C.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
D.
Sasang Station
Sasang Station is a major railway and subway interchange in Busan, South Korea, serving as a key transit hub for both local and intercity travel.
-
E.
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.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fd6ab48190865271e16e8ff669 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbc1b76081909094a9b2f215e58d |
completed | March 28, 2026, 12:38 p.m. |
| NEDg | Description generation | batch_69c7cc5af4f48190a146f7026307bfbe |
completed | March 28, 2026, 12:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7cd0eb36c8190bc8e4265033d214f |
completed | March 28, 2026, 12:43 p.m. |
Created at: March 27, 2026, 2:35 p.m.