Triple
T2299801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanto |
E51702
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Nikko
Nikko is a historic Japanese city in Tochigi Prefecture renowned for its ornate UNESCO-listed shrines, temples, and scenic mountainous landscapes.
|
E253811
|
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: Nikko | Statement: [Kanto, contains, Nikko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nikko Context triple: [Kanto, contains, Nikko]
-
A.
Kamikochi
Kamikochi is a scenic highland valley in Japan’s Northern Alps renowned for its pristine river, mountain views, and popular hiking trails.
-
B.
Takachiho
Takachiho is a town in Miyazaki Prefecture, Japan, famed in mythology as a sacred site of the Japanese creation legends and early imperial origins.
-
C.
Mount Yoshino
Mount Yoshino is a famous mountain in Japan renowned for its thousands of cherry trees that attract large numbers of visitors during the spring blossom season.
-
D.
Kameoka
Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
-
E.
Arima Onsen
Arima Onsen is one of Japan’s oldest and most famous hot spring towns, renowned for its mineral-rich “gold” and “silver” baths nestled in the mountains near Kobe.
- 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: Nikko Triple: [Kanto, contains, Nikko]
Generated description
Nikko is a historic Japanese city in Tochigi Prefecture renowned for its ornate UNESCO-listed shrines, temples, and scenic mountainous landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nikko Target entity description: Nikko is a historic Japanese city in Tochigi Prefecture renowned for its ornate UNESCO-listed shrines, temples, and scenic mountainous landscapes.
-
A.
Kamikochi
Kamikochi is a scenic highland valley in Japan’s Northern Alps renowned for its pristine river, mountain views, and popular hiking trails.
-
B.
Takachiho
Takachiho is a town in Miyazaki Prefecture, Japan, famed in mythology as a sacred site of the Japanese creation legends and early imperial origins.
-
C.
Mount Yoshino
Mount Yoshino is a famous mountain in Japan renowned for its thousands of cherry trees that attract large numbers of visitors during the spring blossom season.
-
D.
Kichijōji
Kichijōji is a popular Tokyo neighborhood known for its trendy shopping streets, vibrant dining and nightlife, and the expansive Inokashira Park.
-
E.
Kameoka
Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc5ec3c948190b47ea763812a1cf5 |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f2e338881908e09d19f469a59ce |
completed | March 9, 2026, 8:05 a.m. |
| NEDg | Description generation | batch_69ae7fd78ee48190990fc7b5034b662b |
completed | March 9, 2026, 8:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae80dadf208190913211329a40b4ee |
completed | March 9, 2026, 8:12 a.m. |
Created at: March 4, 2026, 7:49 p.m.