Triple
T14571976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Sarah Cadogan |
E341938
|
entity |
| Predicate | regionOfActivity |
P82
|
FINISHED |
| Object | Sussex |
E361007
|
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: Sussex | Statement: [Lady Sarah Cadogan, regionOfActivity, Sussex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sussex Context triple: [Lady Sarah Cadogan, regionOfActivity, Sussex]
-
A.
Sussex
Sussex is a traditional British dual-purpose chicken breed valued for both its meat and egg production.
-
B.
Sussex
chosen
Sussex is a historic county in South East England, known for its coastal resorts, rolling South Downs, and rich medieval and maritime heritage.
-
C.
East Sussex
East Sussex is a county in South East England known for its English Channel coastline, the South Downs, and historic towns such as Hastings and Lewes.
-
D.
West Sussex
West Sussex is a county in South East England known for its mix of coastal towns, rural countryside, and historic market settlements.
-
E.
Essex
Essex is a county in the east of England, known for its mix of rural landscapes, historic towns, and proximity to London.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f33b1c8190bb447788bfd28d51 |
completed | April 14, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8aa0e98c81908f6b55a04cd72871 |
completed | May 8, 2026, 7:02 a.m. |
Created at: April 10, 2026, 1:24 a.m.