Triple
T8729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan |
E174
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Tokyo
Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
|
E5560
|
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: Tokyo | Statement: [Japan, capital, Tokyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokyo Context triple: [Japan, capital, Tokyo]
-
A.
Osaka
Osaka is Japan's third-largest city and a major economic, cultural, and historical hub known for its vibrant street food, bustling nightlife, and role as a commercial center in the Kansai region.
-
B.
Beijing
Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
-
C.
Osaka Prefecture
Osaka Prefecture is a populous and economically vital region in Japan’s Kansai area, centered on the city of Osaka and known as a major hub of commerce, industry, and culture.
-
D.
Shibuya, Tokyo, Japan
Shibuya, Tokyo, Japan is a major commercial and entertainment district of Tokyo known for its busy scramble crossing, youth culture, and fashion scene.
-
E.
Kobe
Kobe is a major port city in Japan’s Kansai region, known for its scenic harbor setting, cosmopolitan atmosphere, and famous Kobe beef.
- 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: Tokyo Triple: [Japan, capital, Tokyo]
Generated description
Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tokyo Target entity description: Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
-
A.
Osaka
Osaka is Japan's third-largest city and a major economic, cultural, and historical hub known for its vibrant street food, bustling nightlife, and role as a commercial center in the Kansai region.
-
B.
Beijing
Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
-
C.
Osaka Prefecture
Osaka Prefecture is a populous and economically vital region in Japan’s Kansai area, centered on the city of Osaka and known as a major hub of commerce, industry, and culture.
-
D.
Shibuya, Tokyo, Japan
Shibuya, Tokyo, Japan is a major commercial and entertainment district of Tokyo known for its busy scramble crossing, youth culture, and fashion scene.
-
E.
Kobe
Kobe is a major port city in Japan’s Kansai region, known for its scenic harbor setting, cosmopolitan atmosphere, and famous Kobe beef.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a23ff1903c8190a7d1051b4795eecd |
completed | Feb. 28, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2552ac50c819085e4e45c00cd7956 |
completed | Feb. 28, 2026, 2:38 a.m. |
| NEDg | Description generation | batch_69a2569c52808190acb928405528d174 |
completed | Feb. 28, 2026, 2:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2575b51188190b8fce6ea4e424ce1 |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.