Triple
T802296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Akagi |
E17154
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object | Lake Konuma |
E97478
|
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: Lake Konuma | Statement: [Mount Akagi, hasLake, Lake Konuma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Konuma Context triple: [Mount Akagi, hasLake, Lake Konuma]
-
A.
Lake Ōnuma
chosen
Lake Ōnuma is a volcanic crater lake situated on Mount Akagi in Gunma Prefecture, Japan, known for its scenic beauty and outdoor recreation.
-
B.
Kankaria Lake
Kankaria Lake is a historic, man-made lake in Ahmedabad, India, known for its recreational facilities, zoo, and popular waterfront promenade.
-
C.
Shiga Lakes
Shiga Lakes is a professional basketball team based in Shiga Prefecture, Japan, competing in the country’s top-tier B.League.
-
D.
Lake Kegonsa
Lake Kegonsa is a glacial freshwater lake in south-central Wisconsin that is popular for boating, fishing, and its surrounding state park.
-
E.
Simly Lake
Simly Lake is a major freshwater reservoir and popular recreational spot located in the Margalla Hills near Islamabad, Pakistan.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4aa9e0f0081909d2a89387d6c08e1 |
completed | March 1, 2026, 9:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3b1a81481908c831d1f43b9d014 |
completed | March 4, 2026, 3:14 a.m. |
Created at: March 1, 2026, 7:38 p.m.