Triple
T22368451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince County |
E552973
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kensington
Kensington is a small town in Prince County on Prince Edward Island, Canada, known as a local service and transportation hub for the surrounding rural area.
|
E1533203
|
NE FINISHED |
How this triple was built (4 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: Kensington | Statement: [Prince County, contains, Kensington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kensington Context triple: [Prince County, contains, Kensington]
-
A.
Kensington
Kensington is a historic Philadelphia neighborhood known for its industrial past and ongoing urban redevelopment.
-
B.
Kensington
Kensington is a district in West London, England, known for its affluent residential areas, cultural institutions, and royal associations.
-
C.
Kensington
Kensington is an inner-city suburb of Sydney, Australia, known for hosting the main campus of the University of New South Wales.
-
D.
Kensington
Kensington is a small, affluent residential village located on the North Shore of Long Island in Nassau County, New York.
-
E.
Kensington
Kensington is an inner-city suburb of Melbourne, Victoria, known for its mix of historic workers’ cottages, industrial heritage, and growing residential developments close to the central business district.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kensington Triple: [Prince County, contains, Kensington]
Generated description
Kensington is a small town in Prince County on Prince Edward Island, Canada, known as a local service and transportation hub for the surrounding rural area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kensington Target entity description: Kensington is a small town in Prince County on Prince Edward Island, Canada, known as a local service and transportation hub for the surrounding rural area.
-
A.
Kensington
Kensington is a small, affluent residential village located on the North Shore of Long Island in Nassau County, New York.
-
B.
Kensington
Kensington is a residential suburb of the coastal city of Timaru in the South Island of New Zealand.
-
C.
Kensington
Kensington is a small, affluent unincorporated community in Contra Costa County, California, located in the San Francisco Bay Area.
-
D.
Kensington
Kensington is a district in West London, England, known for its affluent residential areas, cultural institutions, and royal associations.
-
E.
Kensington
Kensington is a residential suburb within the City of Swan in Western Australia, known for its local community amenities and proximity to Perth’s urban areas.
- F. None of above. chosen
Provenance (5 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_69e11e4affcc8190ba7c27d29062558d |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1580229688190a6e5e02b484033f7 |
completed | April 29, 2026, 12:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ae0685c7881908d0ed5fee6d17b5f |
completed | May 18, 2026, 9:48 a.m. |
| NEDg | Description generation | batch_6a0ae4812f4481908ed0e00937419241 |
completed | May 18, 2026, 10:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ae515d6808190bc7bee259c12ca6e |
completed | May 18, 2026, 10:08 a.m. |
Created at: April 16, 2026, 8:44 p.m.