Triple
T20227785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Rangaku in Saga |
E495436
|
entity |
| Predicate | fieldOfStudy |
P3
|
FINISHED |
| Object |
rangaku
Rangaku was the study of Western science and technology in Japan during the Edo period, based primarily on Dutch sources.
|
E1419797
|
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: rangaku | Statement: [School of Rangaku in Saga, fieldOfStudy, rangaku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: rangaku Context triple: [School of Rangaku in Saga, fieldOfStudy, rangaku]
-
A.
Karaiyamai
Karaiyamai is a locality situated within Madhesh Province in southeastern Nepal.
-
B.
Raku
Raku is a multi-paradigm, gradually typed programming language that evolved from the Perl community with a focus on expressiveness, concurrency, and powerful language features.
-
C.
Katunayake
Katunayake is a town in Sri Lanka’s Western Province known primarily as the country’s main international air gateway and an important industrial and transport hub.
-
D.
Ranzan
Ranzan is a town in Saitama Prefecture, Japan, known for its scenic river valleys and rural landscapes northwest of Tokyo.
-
E.
Jūrakuji
Jūrakuji is a Buddhist temple in Japan best known as Temple 7 on the Shikoku 88-temple pilgrimage route.
- 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: rangaku Triple: [School of Rangaku in Saga, fieldOfStudy, rangaku]
Generated description
Rangaku was the study of Western science and technology in Japan during the Edo period, based primarily on Dutch sources.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: rangaku Target entity description: Rangaku was the study of Western science and technology in Japan during the Edo period, based primarily on Dutch sources.
-
A.
Karaiyamai
Karaiyamai is a locality situated within Madhesh Province in southeastern Nepal.
-
B.
Raku
Raku is a multi-paradigm, gradually typed programming language that evolved from the Perl community with a focus on expressiveness, concurrency, and powerful language features.
-
C.
Katunayake
Katunayake is a town in Sri Lanka’s Western Province known primarily as the country’s main international air gateway and an important industrial and transport hub.
-
D.
Ranzan
Ranzan is a town in Saitama Prefecture, Japan, known for its scenic river valleys and rural landscapes northwest of Tokyo.
-
E.
Jūrakuji
Jūrakuji is a Buddhist temple in Japan best known as Temple 7 on the Shikoku 88-temple pilgrimage route.
- 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66fda9428819098467e7e8c547a07 |
completed | April 20, 2026, 6:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a084b8a75b08190ae88c36f275c438b |
completed | May 16, 2026, 10:48 a.m. |
| NEDg | Description generation | batch_6a084d492aec81909178057a7880bb8d |
completed | May 16, 2026, 10:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a085275cefc8190a91ddcb3728bfe3a |
completed | May 16, 2026, 11:18 a.m. |
Created at: April 11, 2026, 11:39 p.m.