Triple
T38759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lexington, Massachusetts |
E766
|
entity |
| Predicate | hasTypeOfClimate |
P193
|
FINISHED |
| Object | humid continental climate |
—
|
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: humid continental climate | Statement: [Lexington, Massachusetts, hasTypeOfClimate, humid continental climate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfClimate Context triple: [Lexington, Massachusetts, hasTypeOfClimate, humid continental climate]
-
A.
hasClimate
chosen
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
B.
hasClimateSystem
Indicates that one entity possesses or is characterized by a particular climate system.
-
C.
containsMajorClimatePhenomenon
Indicates that the subject region or area includes or experiences a significant, large-scale climate-related event or pattern.
-
D.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
E.
hasAverageSpringTemperature
Indicates that an entity is associated with a specific average temperature value measured over the spring season.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24b4d5bd08190a3a48eb26e67768c |
completed | Feb. 28, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a24ab6141881908701106aa97e4735 |
completed | Feb. 28, 2026, 1:53 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.