Triple
T4121097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Somerset County, Pennsylvania |
E92613
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mount Davis |
E75018
|
NE 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: Mount Davis | Statement: [Somerset County, Pennsylvania, contains, Mount Davis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Davis Context triple: [Somerset County, Pennsylvania, contains, Mount Davis]
-
A.
Mount Davis
chosen
Mount Davis is the highest peak in Pennsylvania, located in the Laurel Highlands of the Allegheny Mountains.
-
B.
Mount Wister
Mount Wister is a prominent mountain peak in Wyoming’s Teton Range, known for its rugged terrain and challenging climbing routes.
-
C.
Mount Tennent
Mount Tennent is a prominent mountain in the Australian Capital Territory known for its popular hiking trails and panoramic views within the Namadgi National Park.
-
D.
Sharp Mountain
Sharp Mountain is a prominent natural peak in northern Georgia known for its scenic views and forested slopes within the Appalachian foothills.
-
E.
Wilmot Mountain
Wilmot Mountain is a ski and snowboard area in southeastern Wisconsin known for its family-friendly terrain and proximity to the Chicago and Milwaukee metropolitan areas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed9685f70819086932777aec8d959 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69af0203b8c88190b08dd64800a37168 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf10a00c5c819090fa26ce068033b6 |
completed | March 21, 2026, 9:41 p.m. |
Created at: March 9, 2026, 3:41 p.m.