Triple
T6236852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | central Tokyo |
E139497
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Hibiya
Hibiya is a district in central Tokyo known for its large urban park, theaters, government offices, and proximity to major business and shopping areas.
|
E597966
|
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: Hibiya | Statement: [central Tokyo, contains, Hibiya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hibiya Context triple: [central Tokyo, contains, Hibiya]
-
A.
Ueno
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
B.
Ueno
Ueno is a town in Japan historically known as the birthplace of the renowned haiku poet Matsuo Bashō.
-
C.
Kasumigaseki, Tokyo
Kasumigaseki, Tokyo is a central government district in Chiyoda, Tokyo, known for housing numerous Japanese ministries, agencies, and administrative offices.
-
D.
Kōtō
Kōtō is a special ward in eastern Tokyo, Japan, known for its mix of residential neighborhoods, waterfront areas, and commercial districts.
-
E.
Musashino
Musashino is a suburban city in western Tokyo, Japan, known for the popular Kichijoji district and its blend of residential neighborhoods, shopping areas, and parks.
- 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: Hibiya Triple: [central Tokyo, contains, Hibiya]
Generated description
Hibiya is a district in central Tokyo known for its large urban park, theaters, government offices, and proximity to major business and shopping areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hibiya Target entity description: Hibiya is a district in central Tokyo known for its large urban park, theaters, government offices, and proximity to major business and shopping areas.
-
A.
Ueno
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
B.
Ueno
Ueno is a town in Japan historically known as the birthplace of the renowned haiku poet Matsuo Bashō.
-
C.
Kasumigaseki, Tokyo
Kasumigaseki, Tokyo is a central government district in Chiyoda, Tokyo, known for housing numerous Japanese ministries, agencies, and administrative offices.
-
D.
Kōtō
Kōtō is a special ward in eastern Tokyo, Japan, known for its mix of residential neighborhoods, waterfront areas, and commercial districts.
-
E.
Musashino
Musashino is a suburban city in western Tokyo, Japan, known for the popular Kichijoji district and its blend of residential neighborhoods, shopping areas, and parks.
- 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_69c66373add88190b5fad625c4284c55 |
completed | March 27, 2026, 11:01 a.m. |
| NEDg | Description generation | batch_69c6641f712481908215fd281cd8abf7 |
completed | March 27, 2026, 11:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c66494b114819095a29ab6b699c2ad |
completed | March 27, 2026, 11:05 a.m. |
Created at: March 22, 2026, 4:23 p.m.