Triple
T19116995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AREX |
E467931
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Yeongjong Station
Yeongjong Station is a railway station in Incheon, South Korea, serving the Airport Railroad Express (AREX) line that connects central Seoul with Incheon International Airport.
|
E1383948
|
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: Yeongjong Station | Statement: [AREX, hasStation, Yeongjong Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeongjong Station Context triple: [AREX, hasStation, Yeongjong 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.
Yangjae Station
Yangjae Station is a major subway station in southern Seoul, South Korea, serving as an important transit hub on multiple lines within the city’s metro network.
-
E.
Sangnoksu Station
Sangnoksu Station is a subway station in Ansan, South Korea, serving as part of the Seoul Metropolitan Subway network.
- 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: Yeongjong Station Triple: [AREX, hasStation, Yeongjong Station]
Generated description
Yeongjong Station is a railway station in Incheon, South Korea, serving the Airport Railroad Express (AREX) line that connects central Seoul with Incheon International Airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yeongjong Station Target entity description: Yeongjong Station is a railway station in Incheon, South Korea, serving the Airport Railroad Express (AREX) line that connects central Seoul with Incheon International Airport.
-
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.
Yangjae Station
Yangjae Station is a major subway station in southern Seoul, South Korea, serving as an important transit hub on multiple lines within the city’s metro network.
-
E.
Sangnoksu Station
Sangnoksu Station is a subway station in Ansan, South Korea, serving as part of the Seoul Metropolitan Subway network.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e399a6d8819090a9501ff1637b9d |
completed | April 20, 2026, 8:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07576355b88190beb8960dc69bfc0f |
completed | May 15, 2026, 5:26 p.m. |
| NEDg | Description generation | batch_6a07583fc26481908bd2f00c19f3b4cd |
completed | May 15, 2026, 5:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07594a23ac81908807df4605555d3f |
completed | May 15, 2026, 5:35 p.m. |
Created at: April 10, 2026, 12:05 p.m.