Triple
T7733864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imperial College Business School |
E175331
|
entity |
| Predicate | campus |
P269
|
FINISHED |
| Object | South Kensington |
E313026
|
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: South Kensington | Statement: [Imperial College Business School, campus, South Kensington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: South Kensington Context triple: [Imperial College Business School, campus, South Kensington]
-
A.
South Kensington
chosen
South Kensington is a London Underground station in West London, known for serving the museum district including the Natural History Museum, Science Museum, and Victoria and Albert Museum.
-
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 residential village located on the North Shore of Long Island in Nassau County, New York.
-
D.
Kensington
Kensington is a small, affluent unincorporated community in Contra Costa County, California, located in the San Francisco Bay Area.
-
E.
Kensington
Kensington is a popular inner-city district in Calgary known for its vibrant mix of shops, restaurants, and cultural venues.
- 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70339c4b481909a56ae13f501e794 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d6b1f7648190a0e2fd82ecb9c8b9 |
completed | March 29, 2026, 7:37 a.m. |
Created at: March 27, 2026, 4:06 p.m.