Triple
T10557425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sittingbourne and Sheppey |
E249123
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Kemsley |
E246312
|
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: Kemsley | Statement: [Sittingbourne and Sheppey, containsSettlement, Kemsley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kemsley Context triple: [Sittingbourne and Sheppey, containsSettlement, Kemsley]
-
A.
Kemsley
chosen
Kemsley is a residential suburb and industrial area on the northern edge of Sittingbourne in Kent, England.
-
B.
Kempson
Kempson is an English surname most notably associated with the British acting family that includes actress Rachel Kempson.
-
C.
Southam
Southam is a small historic market town in Warwickshire, England, known for its medieval origins and location near the River Stowe.
-
D.
Hannington
Hannington is a small rural village in Wiltshire, England, known for its traditional English countryside setting and historic character.
-
E.
Kearsley
Kearsley is a town in Greater Manchester, England, historically part of Lancashire and known for its industrial heritage.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5271521a4819086d96e1f183ab07a |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9347ae67081909cb2c3911cb1be67 |
completed | April 10, 2026, 5:33 p.m. |
Created at: April 6, 2026, 12:35 p.m.