Triple
T15406771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Shikotsu |
E368477
|
entity |
| Predicate | maximumDepthRankingInJapan |
P1536
|
FINISHED |
| Object | one of the deepest lakes in Japan |
—
|
LITERAL 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: one of the deepest lakes in Japan | Statement: [Lake Shikotsu, maximumDepthRankingInJapan, one of the deepest lakes in Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumDepthRankingInJapan Context triple: [Lake Shikotsu, maximumDepthRankingInJapan, one of the deepest lakes in Japan]
-
A.
rankingByHeightInJapan
Indicates the relative order of entities based on their height specifically within the context of Japan.
-
B.
rankInJapaneseOrders
Indicates the position or level an entity holds within the hierarchy of Japanese orders, decorations, or honors.
-
C.
rankByCommonnessInJapan
Indicates how items are ordered based on how commonly they occur or are found in Japan.
-
D.
gdpRankInJapan
Indicates the position of an entity in the ordered ranking of GDP values within Japan.
-
E.
depthRank
chosen
Indicates the relative ordering of entities based on how deep or distant they are along a specified depth dimension or hierarchy.
- F. None of above.
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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea36c6881909eaea48e9608897a |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27b8cac8190bfa77698d53c5d1c |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:20 a.m.