Triple
T2726874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Countess of Snowdon |
E60213
|
entity |
| Predicate | territorialDesignation |
P14731
|
FINISHED |
| Object | 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: Snowdon | Statement: [Countess of Snowdon, territorialDesignation, Snowdon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowdon Context triple: [Countess of Snowdon, territorialDesignation, Snowdon]
-
A.
Snowdon
chosen
Snowdon is the tallest and most famous mountain in Wales, renowned for its scenic hiking routes and panoramic views.
-
B.
Tryfan
Tryfan is a distinctive, rugged mountain in Snowdonia, Wales, famed for its jagged profile and popular scrambling routes.
-
C.
Eryri
Eryri is the Welsh name for Snowdonia, a mountainous national park in northwest Wales known for its rugged peaks, lakes, and scenic landscapes.
-
D.
Moel Hebog
Moel Hebog is a prominent mountain in North Wales known for its rugged slopes and panoramic views over the surrounding Snowdonia landscape.
-
E.
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.
- 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_69ab4b75cd908190b691ef0d1801acda |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdace9f308190964a064859612cd6 |
completed | March 7, 2026, 7:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108c5319481909d3e1a237c3f661e |
completed | March 11, 2026, 6:16 a.m. |
Created at: March 6, 2026, 9:56 p.m.