Triple
T23216943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 神戸市 |
E580770
|
entity |
| Predicate | sisterCity |
P1072
|
FINISHED |
| Object | シアトル |
—
|
NE NERFINISHED |
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: シアトル | Statement: [神戸市, sisterCity, シアトル]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: シアトル Context triple: [神戸市, sisterCity, シアトル]
-
A.
Seattle
chosen
Seattle is a major coastal city in the U.S. state of Washington, known for its tech industry, vibrant music and arts scene, and iconic landmarks like the Space Needle.
-
B.
Tukwila
Tukwila is a suburban city just south of Seattle, Washington, known as a regional transportation and retail hub.
-
C.
Tacoma
Tacoma is a small suburb on the Central Coast of New South Wales, Australia, situated along the Wyong River near Tuggerah Lake.
-
D.
Bellevue, Washington
Bellevue, Washington is a rapidly growing city in the Seattle metropolitan area known for its thriving tech industry, upscale downtown, and high quality of life.
-
E.
Tacoma, Washington
Tacoma, Washington is a mid-sized port city in the Pacific Northwest known for its waterfront, industrial history, and vibrant arts and museum scene.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2460389408190be74f41d217799a9 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f19165949c81908e4d66a8a2b0a25a |
completed | April 29, 2026, 5:04 a.m. |
Created at: April 17, 2026, 4:08 p.m.