Triple
T11478556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lytton, British Columbia |
E272083
|
entity |
| Predicate | hasNotableClimateFeature |
P193
|
FINISHED |
| Object | very hot and dry summers |
—
|
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: very hot and dry summers | Statement: [Lytton, British Columbia, hasNotableClimateFeature, very hot and dry summers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableClimateFeature Context triple: [Lytton, British Columbia, hasNotableClimateFeature, very hot and dry summers]
-
A.
hasClimate
chosen
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
B.
hasClimateContext
Indicates that something is associated with, influenced by, or relevant to climate-related conditions, factors, or considerations.
-
C.
hasNotableFeature
Indicates that an entity possesses a specific characteristic, trait, or attribute that is considered significant or noteworthy.
-
D.
hasNotableScenicSpot
Indicates that an entity possesses or is associated with a particularly remarkable or well-known scenic location.
-
E.
hasClimateSystem
Indicates that one entity possesses or is characterized by a particular climate system.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8294e0fe08190b018e840146e27ca |
completed | April 9, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69d8086ecd6c81908f424864857762d6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.