Triple
T17551599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashurst |
E427476
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Ashurst(e) |
—
|
NE NERFINISHED |
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: Ashurst(e) | Statement: [Ashurst, hasVariant, Ashurst(e)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashurst(e) Context triple: [Ashurst, hasVariant, Ashurst(e)]
-
A.
Ashurst
chosen
Ashurst is a given name and surname of English origin, historically borne by various notable figures.
-
B.
Ashurst
Ashurst is a small rural village in Kent, England, known for its countryside setting near Tunbridge Wells.
-
C.
Benson & Forsyth
Benson & Forsyth is a British architectural practice known for its modernist and innovative public building designs, including major museum projects.
-
D.
Sweet & Maxwell
Sweet & Maxwell is a prominent UK-based legal publisher known for producing authoritative law books, journals, and online legal information services.
-
E.
Humphreys & Wharton
Humphreys & Wharton was an early American shipbuilding firm known for constructing major U.S. Navy vessels in the late 18th and early 19th centuries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e454664b348190aea3ad59954b2c91 |
completed | April 19, 2026, 4:04 a.m. |
Created at: April 10, 2026, 5:50 a.m.