Triple
T2356787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Four Mile Trail |
E47573
|
entity |
| Predicate | winterStatus |
P38722
|
FINISHED |
| Object | often closed or partially closed due to snow and ice |
—
|
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: often closed or partially closed due to snow and ice | Statement: [Four Mile Trail, winterStatus, often closed or partially closed due to snow and ice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winterStatus Context triple: [Four Mile Trail, winterStatus, often closed or partially closed due to snow and ice]
-
A.
winterCharacteristic
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
-
B.
wintersIn
Indicates that an entity spends the winter season in a particular place or region.
-
C.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
D.
winterAscentFirstDate
Indicates the calendar date on which the first successful winter ascent of something (such as a route, peak, or wall) took place.
-
E.
summerWinterCycle
Indicates a recurring transition between summer and winter seasons, capturing the cyclical change in conditions or states across these two periods.
- 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_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abcb802da08190980100444010f91e |
completed | March 7, 2026, 6:53 a.m. |
| PD | Predicate disambiguation | batch_69abc5981ce48190a3f7852d28276e11 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcb7ebb708190ba7edffee3c45d8b |
completed | March 7, 2026, 6:53 a.m. |
Created at: March 4, 2026, 7:54 p.m.