Triple
T19122709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cerro Ajusco |
E468087
|
entity |
| Predicate | typicalSnowOccurrence |
P38179
|
FINISHED |
| Object | occasional winter snowfall |
—
|
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: occasional winter snowfall | Statement: [Cerro Ajusco, typicalSnowOccurrence, occasional winter snowfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSnowOccurrence Context triple: [Cerro Ajusco, typicalSnowOccurrence, occasional winter snowfall]
-
A.
hasSnowOccasionally
chosen
Indicates that the subject experiences snowfall at irregular or infrequent intervals rather than regularly or never.
-
B.
hasSnowfallFrequency
Indicates how often snowfall occurs for or at a given entity.
-
C.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
D.
typicalSnowDifficulty
Indicates the usual or characteristic level of difficulty associated with snow conditions for an activity, route, or location.
-
E.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e3c9dfcc819090f8424608892cb3 |
completed | April 20, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69e4b9b085288190b974d649e12e0844 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:05 p.m.