Triple
T7870898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katsushika |
E182733
|
entity |
| Predicate | containsDistrict |
P22582
|
FINISHED |
| Object |
Okudo
Okudo is a residential district within Tokyo’s Katsushika ward, known for its local shopping streets and traditional shitamachi atmosphere.
|
E697720
|
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: Okudo | Statement: [Katsushika, containsDistrict, Okudo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Okudo Context triple: [Katsushika, containsDistrict, Okudo]
-
A.
Kaoru Ushijima
Kaoru Ushijima is a Japanese individual notable enough to be recognized as a namesake of the surname Ushijima.
-
B.
Kita Kojima
Kita Kojima is one of the small, uninhabited islets that form part of the disputed Senkaku Islands in the East China Sea.
-
C.
Makoto Kobayashi
Makoto Kobayashi is a Japanese theoretical physicist renowned for his work on CP violation in the Standard Model, for which he shared the 2008 Nobel Prize in Physics.
-
D.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
E.
Tasuku Honjo
Tasuku Honjo is a Japanese immunologist and Nobel laureate renowned for discovering the PD-1 protein, which led to groundbreaking cancer immunotherapy treatments.
- 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: Okudo Triple: [Katsushika, containsDistrict, Okudo]
Generated description
Okudo is a residential district within Tokyo’s Katsushika ward, known for its local shopping streets and traditional shitamachi atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Okudo Target entity description: Okudo is a residential district within Tokyo’s Katsushika ward, known for its local shopping streets and traditional shitamachi atmosphere.
-
A.
Kaoru Ushijima
Kaoru Ushijima is a Japanese individual notable enough to be recognized as a namesake of the surname Ushijima.
-
B.
Kita Kojima
Kita Kojima is one of the small, uninhabited islets that form part of the disputed Senkaku Islands in the East China Sea.
-
C.
Makoto Kobayashi
Makoto Kobayashi is a Japanese theoretical physicist renowned for his work on CP violation in the Standard Model, for which he shared the 2008 Nobel Prize in Physics.
-
D.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
E.
Tasuku Honjo
Tasuku Honjo is a Japanese immunologist and Nobel laureate renowned for discovering the PD-1 protein, which led to groundbreaking cancer immunotherapy treatments.
- 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_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb384a285881908a5b2de278f9556f |
completed | March 31, 2026, 2:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b6bc7248190adbf4377c52e16a9 |
completed | March 31, 2026, 5:28 a.m. |
| NEDg | Description generation | batch_69cb5f1daac88190a162132bbd40fdc6 |
completed | March 31, 2026, 5:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb768ac1a48190bc6a59a64adf7144 |
completed | March 31, 2026, 7:23 a.m. |
Created at: March 30, 2026, 4:55 p.m.