Triple
T11970774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Kent |
E284912
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Kent |
E5977
|
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: Kent | Statement: [University of Kent, shortName, Kent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kent Context triple: [University of Kent, shortName, Kent]
-
A.
Kent
Kent is a suburban city in King County, Washington, known as a residential and industrial hub within the greater Seattle metropolitan area.
-
B.
Kent
Kent is a villainous saloon owner and primary antagonist in the classic 1939 Western film "Destry Rides Again."
-
C.
Kent
Kent is a small district municipality in British Columbia, Canada, known for its agricultural lands and proximity to the Fraser River.
-
D.
Kent
chosen
Kent is a county in southeastern England known for its historic towns, coastal landscapes, and nickname "the Garden of England."
-
E.
Kent
Kent is a brand of filtered cigarettes historically marketed as a "safer" smoking option and produced by the Lorillard Tobacco Company.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9037d32e88190b1509285dc907d29 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f471e608e881908d45558d6251af9e |
completed | May 1, 2026, 9:27 a.m. |
Created at: April 8, 2026, 9:46 p.m.