Triple
T71132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donner Pass |
E1422
|
entity |
| Predicate | snowfallRecord |
P3555
|
FINISHED |
| Object | among highest in contiguous United States |
—
|
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: among highest in contiguous United States | Statement: [Donner Pass, snowfallRecord, among highest in contiguous United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: snowfallRecord Context triple: [Donner Pass, snowfallRecord, among highest in contiguous United States]
-
A.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
B.
highestPoint
Indicates that one entity is the point with the greatest elevation or height relative to another entity or defined area.
-
C.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
-
D.
lowestPoint
Indicates that one entity is the point with the minimum vertical position or value relative to another entity or within a specified context.
-
E.
highestPointRegion
Indicates that one location is the highest point within a specified region.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24f6997c081908b202f937eb2b14f |
completed | Feb. 28, 2026, 2:14 a.m. |
| PD | Predicate disambiguation | batch_69a24eab7f408190a8275cb82474f575 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a24f65170c8190bc541c8351456a4d |
completed | Feb. 28, 2026, 2:13 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.