Triple
T2156588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowdonia |
E47901
|
entity |
| Predicate | highestPoint |
P210
|
FINISHED |
| Object | Mount Snowdon |
E61320
|
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 Snowdon | Statement: [Snowdonia, highestPoint, Mount Snowdon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Snowdon Context triple: [Snowdonia, highestPoint, Mount Snowdon]
-
A.
Snowdon
chosen
Snowdon is the tallest and most famous mountain in Wales, renowned for its scenic hiking routes and panoramic views.
-
B.
Pen y Fan
Pen y Fan is a prominent mountain in South Wales known for its sweeping views and popularity with hikers and outdoor enthusiasts.
-
C.
Moel Hebog
Moel Hebog is a prominent mountain in North Wales known for its rugged slopes and panoramic views over the surrounding Snowdonia landscape.
-
D.
White Peak
White Peak is the limestone plateau region of England’s Peak District, known for its rolling dales, dry stone walls, and pastoral landscapes.
-
E.
Mount Usborne
Mount Usborne is the tallest mountain in the Falkland Islands, located on East Falkland and known for its rugged, windswept terrain.
- 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_69a88a1d1fd8819088b34990d69a712f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe67d6888190b8d3c07527c8f80e |
completed | March 7, 2026, 5:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6af04e908190a8eaee7500e0b1af |
completed | March 9, 2026, 6:38 a.m. |
Created at: March 4, 2026, 7:44 p.m.