Triple
T14167052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 千代田区 |
E351107
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
神田
神田 is a historic commercial and residential district in central Tokyo known for its traditional neighborhoods, bookstores, and proximity to major business areas.
|
E1083109
|
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: 神田 | Statement: [千代田区, contains, 神田]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 神田 Context triple: [千代田区, contains, 神田]
-
A.
池田
池田 is a common Japanese surname and place name associated with various regions, historical figures, and businesses in Japan.
-
B.
源田 実
源田 実 was a Japanese naval aviator and strategist best known as a key planner of the attack on Pearl Harbor and later a postwar politician.
-
C.
陸田
陸田 is a Japanese surname written with the kanji characters for “land” (陸) and “rice field” (田).
-
D.
辰野金吾
辰野金吾 was a prominent Japanese architect of the Meiji and Taishō eras, best known for designing landmark Western-style buildings such as Tokyo Station.
-
E.
佐藤
佐藤 is a very common Japanese surname, often romanized as Satō or Sato.
- 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: 神田 Triple: [千代田区, contains, 神田]
Generated description
神田 is a historic commercial and residential district in central Tokyo known for its traditional neighborhoods, bookstores, and proximity to major business areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 神田 Target entity description: 神田 is a historic commercial and residential district in central Tokyo known for its traditional neighborhoods, bookstores, and proximity to major business areas.
-
A.
池田
池田 is a common Japanese surname and place name associated with various regions, historical figures, and businesses in Japan.
-
B.
源田 実
源田 実 was a Japanese naval aviator and strategist best known as a key planner of the attack on Pearl Harbor and later a postwar politician.
-
C.
陸田
陸田 is a Japanese surname written with the kanji characters for “land” (陸) and “rice field” (田).
-
D.
辰野金吾
辰野金吾 was a prominent Japanese architect of the Meiji and Taishō eras, best known for designing landmark Western-style buildings such as Tokyo Station.
-
E.
佐藤
佐藤 is a very common Japanese surname, often romanized as Satō or Sato.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b355f08190864c7322bbcb766d |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7f57ad88190aeb8ee0f834bfa20 |
completed | May 7, 2026, 8:37 p.m. |
| NEDg | Description generation | batch_69fcfdcbd53c81909a347e26b30f9c0b |
completed | May 7, 2026, 9:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcfe9099508190bafd65d0d00129f0 |
completed | May 7, 2026, 9:05 p.m. |
Created at: April 10, 2026, 1 a.m.