Triple
T6408110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Town of North Hempstead |
E127637
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Kensington
Kensington is a small, affluent residential village located on the North Shore of Long Island in Nassau County, New York.
|
E606471
|
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: [Town of North Hempstead, containsSettlement, Kensington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kensington Context triple: [Town of North Hempstead, containsSettlement, 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 a small, affluent unincorporated community in Contra Costa County, California, located in the San Francisco Bay Area.
-
D.
Kensington
Kensington is a popular inner-city district in Calgary known for its vibrant mix of shops, restaurants, and cultural venues.
-
E.
Kensington
Kensington is an inner-city suburb of Sydney, Australia, known for hosting the main campus of the University of New South Wales.
- 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: [Town of North Hempstead, containsSettlement, Kensington]
Generated description
Kensington is a small, affluent residential village located on the North Shore of Long Island in Nassau County, New York.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kensington Target entity description: Kensington is a small, affluent residential village located on the North Shore of Long Island in Nassau County, New York.
-
A.
Kensington
Kensington is a residential neighborhood in central Brooklyn, New York City, known for its diverse population and mix of apartment buildings and single-family homes.
-
B.
Kensington
Kensington is a small, affluent unincorporated community in Contra Costa County, California, located in the San Francisco Bay Area.
-
C.
Kensington
Kensington is a district in West London, England, known for its affluent residential areas, cultural institutions, and royal associations.
-
D.
Kensington
Kensington is a residential suburb of the coastal city of Timaru in the South Island of New Zealand.
-
E.
Kensington
Kensington is an inner-city suburb of Sydney, Australia, known for hosting the main campus of the University of New South Wales.
- 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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068ccd804819097b106604372c14a |
completed | March 22, 2026, 10:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e40980d08190930db5f9ce577a0e |
completed | March 27, 2026, 8:09 p.m. |
| NEDg | Description generation | batch_69c6e50db2cc8190932c4d44257acb83 |
completed | March 27, 2026, 8:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6e5eacefc819092d0e9f79d90c4a6 |
completed | March 27, 2026, 8:17 p.m. |
Created at: March 22, 2026, 4:41 p.m.