Triple
T292799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hartford, Connecticut, United States |
E6029
|
entity |
| Predicate | averageAnnualSnowfall |
P10513
|
FINISHED |
| Object | ~50 in |
—
|
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: ~50 in | Statement: [Hartford, Connecticut, United States, averageAnnualSnowfall, ~50 in]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageAnnualSnowfall Context triple: [Hartford, Connecticut, United States, averageAnnualSnowfall, ~50 in]
-
A.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
-
B.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
C.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
D.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
E.
elevationYear
Indicates the year in which an entity was elevated to a higher status, rank, or designation.
- F. None of above. chosen
Provenance (4 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea0dd1dc8190aecd5afdeb2fd74b |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e934b4408190b53a17f57a02df65 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea07e3bc8190bae593b3264de211 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.