Triple
T6553378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abeno Harukas |
E152381
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Abeno district |
E152381
|
NE FINISHED |
How this triple was built (2 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: Abeno district | Statement: [Abeno Harukas, locatedIn, Abeno district]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abeno district Context triple: [Abeno Harukas, locatedIn, Abeno district]
-
A.
Abeno district
chosen
Abeno district is a major commercial and residential area in Osaka, Japan, known for its large shopping complexes and the landmark Abeno Harukas skyscraper.
-
B.
Umeda district
Umeda district is a major commercial and transportation hub in Osaka, Japan, known for its skyscrapers, shopping complexes, and extensive train and subway connections.
-
C.
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.
-
D.
Nishi-ku
Nishi-ku is a ward in Fukuoka, Japan, known for its coastal areas, residential neighborhoods, and access to both urban amenities and natural scenery.
-
E.
Nankai District
Nankai District is a central urban district of Tianjin, China, known for its educational institutions, historical sites, and commercial areas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c688058d6881908c19b309cc55dbfa |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae0847d88190b38f9d7dba0faae1 |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b89caf148190a2959698e5849e12 |
completed | March 28, 2026, 11:16 a.m. |
Created at: March 27, 2026, 1:51 p.m.