Triple
T6236848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | central Tokyo |
E139497
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Toyosu
Toyosu is a modern waterfront district in Tokyo best known for its large-scale urban redevelopment and the Toyosu Market, which replaced the historic Tsukiji fish market.
|
E598634
|
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: Toyosu | Statement: [central Tokyo, contains, Toyosu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toyosu Context triple: [central Tokyo, contains, Toyosu]
-
A.
Hayama
Hayama is a coastal town in Kanagawa Prefecture, Japan, known for its beaches, scenic views of Sagami Bay, and as a site of an Imperial Villa.
-
B.
Hatanodai
Hatanodai is a residential neighborhood in Tokyo, Japan, known for its local shopping streets and convenient rail connections.
-
C.
Tomakomai
Tomakomai is an industrial port city on the southern coast of Hokkaido, Japan, known for its paper manufacturing, shipping, and ferry connections.
-
D.
Atami
Atami is a coastal hot spring resort city in Shizuoka Prefecture, Japan, known for its onsen, beaches, and proximity to Tokyo.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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: Toyosu Triple: [central Tokyo, contains, Toyosu]
Generated description
Toyosu is a modern waterfront district in Tokyo best known for its large-scale urban redevelopment and the Toyosu Market, which replaced the historic Tsukiji fish market.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Toyosu Target entity description: Toyosu is a modern waterfront district in Tokyo best known for its large-scale urban redevelopment and the Toyosu Market, which replaced the historic Tsukiji fish market.
-
A.
Hayama
Hayama is a coastal town in Kanagawa Prefecture, Japan, known for its beaches, scenic views of Sagami Bay, and as a site of an Imperial Villa.
-
B.
Hatanodai
Hatanodai is a residential neighborhood in Tokyo, Japan, known for its local shopping streets and convenient rail connections.
-
C.
Tomakomai
Tomakomai is an industrial port city on the southern coast of Hokkaido, Japan, known for its paper manufacturing, shipping, and ferry connections.
-
D.
Atami
Atami is a coastal hot spring resort city in Shizuoka Prefecture, Japan, known for its onsen, beaches, and proximity to Tokyo.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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_69c008b0e7ac8190808a59573ee646f3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063021258819093a9237041816638 |
completed | March 22, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c67c3b6cdc8190ad9e10957e301a57 |
completed | March 27, 2026, 12:46 p.m. |
| NEDg | Description generation | batch_69c67cb5b2a48190b04709f80c98d77d |
completed | March 27, 2026, 12:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c67dfc51048190adeccd569c85da85 |
completed | March 27, 2026, 12:54 p.m. |
Created at: March 22, 2026, 4:23 p.m.