Triple
T1756031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fukuoka |
E38549
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Higashi-ku
Higashi-ku is a ward in the city of Fukuoka, Japan, known for its coastal location, residential areas, and educational institutions.
|
E361288
|
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: Higashi-ku | Statement: [Fukuoka, hasDistrict, Higashi-ku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Higashi-ku Context triple: [Fukuoka, hasDistrict, Higashi-ku]
-
A.
Nishi-ku
Nishi-ku is a central ward of Yokohama, Japan, known as a major commercial and business district that includes the Minato Mirai 21 waterfront area.
-
B.
Naniwa-ku
Naniwa-ku is a central ward of Osaka, Japan, known for its bustling entertainment districts, shopping streets, and iconic landmarks such as Tsutenkaku Tower.
-
C.
Nankai District
Nankai District is a central urban district of Tianjin, China, known for its educational institutions, historical sites, and commercial areas.
-
D.
Chūō-ku
Chūō-ku is a central ward of Osaka, Japan, known as a major commercial and entertainment hub featuring famous landmarks, shopping streets, and nightlife areas.
-
E.
Chūō-ku
Chūō-ku is a central ward of Fukuoka City in Japan, known as a major commercial, entertainment, and administrative hub.
- 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: Higashi-ku Triple: [Fukuoka, hasDistrict, Higashi-ku]
Generated description
Higashi-ku is a ward in the city of Fukuoka, Japan, known for its coastal location, residential areas, and educational institutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Higashi-ku Target entity description: Higashi-ku is a ward in the city of Fukuoka, Japan, known for its coastal location, residential areas, and educational institutions.
-
A.
Nishi-ku
Nishi-ku is a central ward of Yokohama, Japan, known as a major commercial and business district that includes the Minato Mirai 21 waterfront area.
-
B.
Naniwa-ku
Naniwa-ku is a central ward of Osaka, Japan, known for its bustling entertainment districts, shopping streets, and iconic landmarks such as Tsutenkaku Tower.
-
C.
Nankai District
Nankai District is a central urban district of Tianjin, China, known for its educational institutions, historical sites, and commercial areas.
-
D.
Chūō-ku
Chūō-ku is a central ward of Fukuoka City in Japan, known as a major commercial, entertainment, and administrative hub.
-
E.
Chūō-ku
Chūō-ku is a central ward of Osaka, Japan, known as a major commercial and entertainment hub featuring famous landmarks, shopping streets, and nightlife areas.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa643b623081908064be75758ec5de |
completed | March 6, 2026, 5:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b367d99b548190981f471e167198da |
completed | March 13, 2026, 1:26 a.m. |
| NEDg | Description generation | batch_69b36b9562a881908e424260db3abeac |
completed | March 13, 2026, 1:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b36bf6c16481908f95e5f2c2aed5ed |
completed | March 13, 2026, 1:44 a.m. |
Created at: March 4, 2026, 7:31 p.m.