Triple
T15406796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Shikotsu |
E368477
|
entity |
| Predicate | freezingCharacteristic |
P19069
|
FINISHED |
| Object | rarely freezes completely |
—
|
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: rarely freezes completely | Statement: [Lake Shikotsu, freezingCharacteristic, rarely freezes completely]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: freezingCharacteristic Context triple: [Lake Shikotsu, freezingCharacteristic, rarely freezes completely]
-
A.
freezesOver
chosen
Indicates that a liquid surface becomes solid due to low temperatures, typically forming a layer of ice over it.
-
B.
hasTypicalFreezingPoint
Indicates the temperature at which a substance normally changes from liquid to solid under standard conditions.
-
C.
frozenIn
Indicates that one entity is immobilized or preserved in a solid, frozen state within or by another entity.
-
D.
neverFreezes
Indicates that the subject is never in a state of freezing, i.e., it does not reach or experience freezing conditions under any circumstances.
-
E.
winterCharacteristic
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
- 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.