Triple
T8781018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibuya |
E208724
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Sakuragaokacho
Sakuragaokacho is a neighborhood in Tokyo’s Shibuya ward known for its urban atmosphere and proximity to Shibuya Station.
|
E819169
|
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: Sakuragaokacho | Statement: [Shibuya, contains, Sakuragaokacho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sakuragaokacho Context triple: [Shibuya, contains, Sakuragaokacho]
-
A.
Komagome
Komagome is a residential and commercial neighborhood in Tokyo known for its traditional atmosphere, historic temples, and the renowned Rikugien Garden.
-
B.
Nagatacho
Nagatacho is a central district in Tokyo, Japan, known as the political heart of the country and home to key government institutions such as the National Diet Building.
-
C.
Omotesandō
Omotesandō is a fashionable, tree-lined avenue in Tokyo known for its high-end boutiques, modern architecture, and trendy cafés.
-
D.
Ueno
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
E.
Ueno
Ueno is a town in Japan historically known as the birthplace of the renowned haiku poet Matsuo Bashō.
- 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: Sakuragaokacho Triple: [Shibuya, contains, Sakuragaokacho]
Generated description
Sakuragaokacho is a neighborhood in Tokyo’s Shibuya ward known for its urban atmosphere and proximity to Shibuya Station.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sakuragaokacho Target entity description: Sakuragaokacho is a neighborhood in Tokyo’s Shibuya ward known for its urban atmosphere and proximity to Shibuya Station.
-
A.
Komagome
Komagome is a residential and commercial neighborhood in Tokyo known for its traditional atmosphere, historic temples, and the renowned Rikugien Garden.
-
B.
Nagatacho
Nagatacho is a central district in Tokyo, Japan, known as the political heart of the country and home to key government institutions such as the National Diet Building.
-
C.
Omotesandō
Omotesandō is a fashionable, tree-lined avenue in Tokyo known for its high-end boutiques, modern architecture, and trendy cafés.
-
D.
Ueno
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
E.
Ueno
Ueno is a town in Japan historically known as the birthplace of the renowned haiku poet Matsuo Bashō.
- 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_69ca835fbee88190bf625939bac48d7f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f55b7b08190ab3e18cd634a144b |
completed | March 31, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bc96f7288190ab8d1bbf9d428903 |
completed | April 5, 2026, 1:36 a.m. |
| NEDg | Description generation | batch_69d1bd7ee790819093b0aec59e22c6d3 |
completed | April 5, 2026, 1:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1bde561f88190bf8a0126096ee6bf |
completed | April 5, 2026, 1:41 a.m. |
Created at: March 30, 2026, 6:42 p.m.