Triple
T20654922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musashi-Koyama |
E507597
|
entity |
| Predicate | hasNearbyArea |
P4647
|
FINISHED |
| Object |
Fudomae
Fudomae is a neighborhood in Tokyo, Japan, known for its convenient residential setting and proximity to central city areas.
|
E1443316
|
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: Fudomae | Statement: [Musashi-Koyama, hasNearbyArea, Fudomae]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fudomae Context triple: [Musashi-Koyama, hasNearbyArea, Fudomae]
-
A.
Shōji-ko
Shōji-ko is one of the Fuji Five Lakes in Yamanashi Prefecture, Japan, known for its scenic views of Mount Fuji and relatively undeveloped, tranquil surroundings.
-
B.
Fukusaki
Fukusaki is a town in Hyōgo Prefecture, Japan, known for its rural setting and association with folklorist Kunio Yanagita.
-
C.
Manyo-no-Yu
Manyo-no-Yu is a Japanese chain of hot spring and relaxation facilities offering onsen baths, spa services, and traditional-style accommodations.
-
D.
Shōji
Shōji is a Japanese masculine given name that can be written with various kanji and is borne by numerous notable figures in fields such as the military, arts, and sports.
-
E.
Dōshō
Dōshō was a 7th-century Japanese Buddhist monk known for studying in China under Xuanzang and helping establish the Hossō (Yogācāra) school in Japan.
- 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: Fudomae Triple: [Musashi-Koyama, hasNearbyArea, Fudomae]
Generated description
Fudomae is a neighborhood in Tokyo, Japan, known for its convenient residential setting and proximity to central city areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fudomae Target entity description: Fudomae is a neighborhood in Tokyo, Japan, known for its convenient residential setting and proximity to central city areas.
-
A.
Shōji-ko
Shōji-ko is one of the Fuji Five Lakes in Yamanashi Prefecture, Japan, known for its scenic views of Mount Fuji and relatively undeveloped, tranquil surroundings.
-
B.
Fukusaki
Fukusaki is a town in Hyōgo Prefecture, Japan, known for its rural setting and association with folklorist Kunio Yanagita.
-
C.
Manyo-no-Yu
Manyo-no-Yu is a Japanese chain of hot spring and relaxation facilities offering onsen baths, spa services, and traditional-style accommodations.
-
D.
Shōji
Shōji is a Japanese masculine given name that can be written with various kanji and is borne by numerous notable figures in fields such as the military, arts, and sports.
-
E.
Dōshō
Dōshō was a 7th-century Japanese Buddhist monk known for studying in China under Xuanzang and helping establish the Hossō (Yogācāra) school in Japan.
- 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2ec90e881909250884483429acf |
completed | April 20, 2026, 11:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08c5978a3881908ec69d554791a6ce |
completed | May 16, 2026, 7:29 p.m. |
| NEDg | Description generation | batch_6a08c6b3abf881909c9cb0e6ec85b84f |
completed | May 16, 2026, 7:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08c778429081909235b628ba07d574 |
completed | May 16, 2026, 7:37 p.m. |
Created at: April 16, 2026, 11:43 a.m.