Triple
T22533349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 東京都港区 |
E557098
|
entity |
| Predicate | containsDistrict |
P22582
|
FINISHED |
| Object | 青山 |
—
|
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: 青山 | Statement: [東京都港区, containsDistrict, 青山]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 青山 Context triple: [東京都港区, containsDistrict, 青山]
-
A.
青山
chosen
青山は、東京都港区と渋谷区にまたがる洗練された商業エリアで、高級ブティックやカフェ、ギャラリーが集まるおしゃれな街として知られています。
-
B.
Wuling Mountain
Wuling Mountain is a prominent peak in northern China known as the highest summit of the Yan Mountains range.
-
C.
Lianhua Mountain
Lianhua Mountain is a notable scenic hill in Shenzhen, China, known for its panoramic city views, green spaces, and cultural landmarks.
-
D.
Mianshan Mountain
Mianshan Mountain is a scenic and historically significant mountain in Shanxi Province, China, known for its dramatic cliffs, temples, and cultural heritage sites.
-
E.
Tianshou Mountain
Tianshou Mountain is a notable mountain in China, recognized for its scenic landscapes and cultural significance.
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed88cf08190ae7e5b6bf9a80372 |
completed | April 29, 2026, 1:28 a.m. |
Created at: April 16, 2026, 8:51 p.m.