Triple
T26897103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Freeze |
E677924
|
entity |
| Predicate | effectOnWeather |
P32056
|
FINISHED |
| Object | sudden drop in temperature |
—
|
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: sudden drop in temperature | Statement: [Great Freeze, effectOnWeather, sudden drop in temperature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnWeather Context triple: [Great Freeze, effectOnWeather, sudden drop in temperature]
-
A.
hasSignificantWeatherInfluence
Indicates that one entity exerts a substantial impact on the weather conditions or patterns experienced by another entity or region.
-
B.
associatedWithWeather
chosen
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
C.
skyConditionAdvantage
Indicates that certain sky or weather conditions provide a beneficial effect or favorable advantage to an entity or activity.
-
D.
typicalWeatherFeature
Indicates a weather condition or pattern that commonly characterizes a place or time period.
-
E.
snowInfluence
Indicates that one entity affects, alters, or contributes to the presence, behavior, or characteristics of snow in relation to another entity or context.
- 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_69eee9befee48190a26f214faa867be7 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 27, 2026, 5:48 a.m.