Triple
T9209938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagasaki Prefecture |
E221086
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Saikai
Saikai is a coastal city in western Japan known for its scenic islands, inlets, and marine landscapes within Nagasaki Prefecture.
|
E785269
|
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: Saikai | Statement: [Nagasaki Prefecture, hasCity, Saikai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saikai Context triple: [Nagasaki Prefecture, hasCity, Saikai]
-
A.
Shiki, Saitama
Shiki, Saitama is a city in Saitama Prefecture, Japan, known as a residential suburb within the Greater Tokyo metropolitan area.
-
B.
Aishō
Aishō is a town in Shiga Prefecture, Japan, known for its rural character and historical sites.
-
C.
Sadaijin
Sadaijin was a high-ranking ministerial post in Japan’s imperial court, typically overseeing the left side of the government and ranking just below the chancellor in the classical ritsuryō system.
-
D.
Amagi
Amagi was an Imperial Japanese Navy aircraft carrier planned as part of Japan’s early carrier force before being cancelled due to damage from the 1923 Great Kantō earthquake.
-
E.
Shikai
Shikai is the given name of Yuan Shikai, the Chinese military and political leader who became the first president of the Republic of China.
- 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: Saikai Triple: [Nagasaki Prefecture, hasCity, Saikai]
Generated description
Saikai is a coastal city in western Japan known for its scenic islands, inlets, and marine landscapes within Nagasaki Prefecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saikai Target entity description: Saikai is a coastal city in western Japan known for its scenic islands, inlets, and marine landscapes within Nagasaki Prefecture.
-
A.
Shiki, Saitama
Shiki, Saitama is a city in Saitama Prefecture, Japan, known as a residential suburb within the Greater Tokyo metropolitan area.
-
B.
Aishō
Aishō is a town in Shiga Prefecture, Japan, known for its rural character and historical sites.
-
C.
Sadaijin
Sadaijin was a high-ranking ministerial post in Japan’s imperial court, typically overseeing the left side of the government and ranking just below the chancellor in the classical ritsuryō system.
-
D.
Amagi
Amagi was an Imperial Japanese Navy aircraft carrier planned as part of Japan’s early carrier force before being cancelled due to damage from the 1923 Great Kantō earthquake.
-
E.
Shikai
Shikai is the given name of Yuan Shikai, the Chinese military and political leader who became the first president of the Republic of China.
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9b3c8c081909a688ce699928fc0 |
completed | April 1, 2026, 8:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d065efcb64819097d4624bd9e423d2 |
completed | April 4, 2026, 1:14 a.m. |
| NEDg | Description generation | batch_69d06770ccf08190b00bf35c16a80071 |
completed | April 4, 2026, 1:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d06864b8c48190b8e08ab9c1c85c9a |
completed | April 4, 2026, 1:24 a.m. |
Created at: March 30, 2026, 7:26 p.m.