Triple
T20063534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kobe, Hyogo, Japan |
E499546
|
entity |
| Predicate | hasWard |
P14475
|
FINISHED |
| Object |
Chuo-ku
Chuo-ku is a central ward of Kobe in Hyogo Prefecture, Japan, known for its commercial districts, government offices, and urban waterfront areas.
|
E1412666
|
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: Chuo-ku | Statement: [Kobe, Hyogo, Japan, hasWard, Chuo-ku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chuo-ku Context triple: [Kobe, Hyogo, Japan, hasWard, Chuo-ku]
-
A.
Chuo Ward
Chuo Ward is a central administrative district of Kumamoto City in Japan, known for its role as a key commercial and civic hub of the area.
-
B.
Chuo Ward
Chuo Ward is a central special ward of Tokyo, Japan, known for its major commercial districts like Ginza and Nihonbashi and its role as a key business and shopping hub.
-
C.
Higashi-ku
Higashi-ku is a ward in the city of Fukuoka, Japan, known for its coastal location, residential areas, and educational institutions.
-
D.
Seo District
Seo District is a western coastal district of Incheon, South Korea, known for its industrial complexes, port facilities, and growing residential areas.
-
E.
Bunkyō-ku
Bunkyō-ku is a central Tokyo ward known for its universities, cultural institutions, and quiet residential neighborhoods.
- 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: Chuo-ku Triple: [Kobe, Hyogo, Japan, hasWard, Chuo-ku]
Generated description
Chuo-ku is a central ward of Kobe in Hyogo Prefecture, Japan, known for its commercial districts, government offices, and urban waterfront areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chuo-ku Target entity description: Chuo-ku is a central ward of Kobe in Hyogo Prefecture, Japan, known for its commercial districts, government offices, and urban waterfront areas.
-
A.
Chuo Ward
Chuo Ward is a central administrative district of Kumamoto City in Japan, known for its role as a key commercial and civic hub of the area.
-
B.
Chuo Ward
Chuo Ward is a central special ward of Tokyo, Japan, known for its major commercial districts like Ginza and Nihonbashi and its role as a key business and shopping hub.
-
C.
Higashi-ku
Higashi-ku is a ward in the city of Fukuoka, Japan, known for its coastal location, residential areas, and educational institutions.
-
D.
Seo District
Seo District is a western coastal district of Incheon, South Korea, known for its industrial complexes, port facilities, and growing residential areas.
-
E.
Bunkyō-ku
Bunkyō-ku is a central Tokyo ward known for its universities, cultural institutions, and quiet residential neighborhoods.
- 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_69da6276bcf48190aabbf279192a5fb4 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66377b6b48190a0a37279f285123e |
completed | April 20, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a082dc8db54819092ced7c6a506afaa |
completed | May 16, 2026, 8:41 a.m. |
| NEDg | Description generation | batch_6a082e93a1e88190bac5daae12ece9f2 |
completed | May 16, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a082f334dd481908edb6ea3ae07e0a7 |
completed | May 16, 2026, 8:47 a.m. |
Created at: April 11, 2026, 3:39 p.m.