Triple
T8459469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Pickering |
E200004
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object | Claremont |
E436671
|
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: Claremont | Statement: [City of Pickering, hasNeighbourhood, Claremont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claremont Context triple: [City of Pickering, hasNeighbourhood, Claremont]
-
A.
Claremont
chosen
Claremont is a residential neighbourhood within the city of Pickering in Ontario, Canada.
-
B.
Claremont
Claremont is a suburb of Hobart in Tasmania, Australia, known for its residential character and proximity to the Derwent River.
-
C.
Claremont
Claremont is a residential area within the City of Salford in Greater Manchester, England.
-
D.
Claremont
Claremont is a small town located in the rural interior of Saint Ann Parish on Jamaica’s north coast.
-
E.
Claremont, California
Claremont, California is a suburban city in eastern Los Angeles County known for its consortium of prestigious colleges, tree-lined streets, and vibrant academic community.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe49e036c8190abb50ca272266ca6 |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce88bc6b008190a93fbd41089d6c72 |
completed | April 2, 2026, 3:18 p.m. |
Created at: March 30, 2026, 6:10 p.m.