Triple
T13720947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silver Sands Beach |
E329032
|
entity |
| Predicate | hasNearbySuburb |
P41355
|
FINISHED |
| Object | Silver Sands |
E228607
|
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: Silver Sands | Statement: [Silver Sands Beach, hasNearbySuburb, Silver Sands]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silver Sands Context triple: [Silver Sands Beach, hasNearbySuburb, Silver Sands]
-
A.
Silver Sands
chosen
Silver Sands is a popular sandy beach near Aberdour in Fife, Scotland, known for its scenic coastal views and recreational facilities.
-
B.
Sunny Sands
Sunny Sands is a popular sandy beach in Folkestone, Kent, known for its family-friendly atmosphere and traditional seaside charm.
-
C.
Silver Beach
Silver Beach is a famous scenic seaside resort area in Beihai, China, renowned for its fine white sand and gentle coastal waters.
-
D.
Summerland Beach
Summerland Beach is a popular coastal spot on Phillip Island in Victoria, Australia, best known for its scenic shoreline and nearby penguin viewing attractions.
-
E.
Sugar Beach
Sugar Beach is a popular urban beach park on Toronto’s waterfront known for its white sand, pink umbrellas, and views of Lake Ontario.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f3b46481909ceedfa78e9ca92b |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5c4f848190993f829824eabbef |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.