Triple
T3862458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simplon Pass |
E90169
|
entity |
| Predicate | hasWinterCondition |
P38722
|
FINISHED |
| Object | snowy winters |
—
|
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: snowy winters | Statement: [Simplon Pass, hasWinterCondition, snowy winters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWinterCondition Context triple: [Simplon Pass, hasWinterCondition, snowy winters]
-
A.
hasSnowAndIce
Indicates that the subject is covered with or contains both snow and ice.
-
B.
requiresEquipmentInWinter
Indicates that performing the related activity or fulfilling the related condition necessitates the use of specific equipment during winter.
-
C.
winterStatus
chosen
Indicates the condition, phase, or circumstances associated with the winter season for a given entity or context.
-
D.
winterCharacteristic
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
-
E.
hasSnowIn
Indicates that snow is present or occurs within a specified location or region.
- 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_69aed95b3c088190a8f85d19e6070599 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec2417648190ad010189d304d119 |
completed | March 9, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69aee752c8a48190a670f73ed0bf1e61 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:19 p.m.