Triple
T13036086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 42 (school) |
E326563
|
entity |
| Predicate | hasCampus |
P116
|
FINISHED |
| Object |
42 Seoul
42 Seoul is the South Korean campus of the global, tuition-free 42 coding school network, offering peer-to-peer, project-based programming education.
|
E19209
|
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: 42 Seoul | Statement: [42 (school), hasCampus, 42 Seoul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 42 Seoul Context triple: [42 (school), hasCampus, 42 Seoul]
-
A.
Daegu, South Korea
Daegu, South Korea is a major city in the southeastern part of the country known for its role as an industrial, cultural, and educational center.
-
B.
Seoul
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
C.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
D.
Busan, South Korea
Busan, South Korea is the country’s second-largest city and a major coastal hub known for its busy port, beaches, and international film festival.
-
E.
Gwangju
Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
- 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: 42 Seoul Triple: [42 (school), hasCampus, 42 Seoul]
Generated description
42 Seoul is the South Korean campus of the global, tuition-free 42 coding school network, offering peer-to-peer, project-based programming education.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 42 Seoul Target entity description: 42 Seoul is the South Korean campus of the global, tuition-free 42 coding school network, offering peer-to-peer, project-based programming education.
-
A.
Daegu, South Korea
Daegu, South Korea is a major city in the southeastern part of the country known for its role as an industrial, cultural, and educational center.
-
B.
Seoul
chosen
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
C.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
D.
Busan, South Korea
Busan, South Korea is the country’s second-largest city and a major coastal hub known for its busy port, beaches, and international film festival.
-
E.
Gwangju
Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
- F. None of above.
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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c1ee7048190b2571364b25bd49d |
completed | May 8, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fd4cc76178819086fb9a9b6b5cfd05 |
completed | May 8, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd4dcc41c481908f0d7e05c4c176ee |
completed | May 8, 2026, 2:43 a.m. |
Created at: April 9, 2026, 8:55 p.m.