Triple
T8354643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 中部地方 |
E196653
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | 岐阜市 |
E572412
|
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: 岐阜市 | Statement: [中部地方, hasMajorCity, 岐阜市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 岐阜市 Context triple: [中部地方, hasMajorCity, 岐阜市]
-
A.
岐阜市
chosen
岐阜市 is the capital city of Gifu Prefecture in central Japan, known as a regional commercial hub with historical ties to samurai-era Gifu Castle and traditional cormorant fishing on the Nagara River.
-
B.
柏崎市
柏崎市は、新潟県中越地方に位置し、日本海に面したエネルギー産業や海水浴場で知られる都市です。
-
C.
丹波市
丹波市 is a rural city in central Hyōgo Prefecture, Japan, known for its historic castle town atmosphere, agricultural products, and scenic natural landscapes.
-
D.
福知山市
福知山市 is a city in northern Kyoto Prefecture, Japan, known as a regional commercial and transportation hub with a mix of historical sites and rural landscapes.
-
E.
伊勢市
伊勢市 is a city in Mie Prefecture, Japan, best known as the home of the sacred Ise Grand Shrine, one of Shinto’s most important religious sites.
- 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_69ca82f08b348190bfb7881944bbff6f |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb8048edb88190a1980ad74818b898 |
completed | March 31, 2026, 8:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc75e94288190ba1905dd4ca172dd |
completed | April 2, 2026, 1:33 a.m. |
Created at: March 30, 2026, 5:59 p.m.