Triple
T1930300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaughan |
E40928
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object | Maple |
E147635
|
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: Maple | Statement: [Vaughan, hasNeighbourhood, Maple]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maple Context triple: [Vaughan, hasNeighbourhood, Maple]
-
A.
Maples
Maples is the surname of Marla Maples, an American actress and television personality best known as the second wife of former U.S. President Donald Trump.
-
B.
Maple Library
chosen
Maple Library is a public community library serving residents of the Maple neighbourhood in Vaughan, Ontario.
-
C.
Maple GO Station
Maple GO Station is a commuter rail station in Maple, Ontario, serving as a local stop on GO Transit's regional rail network in the Greater Toronto Area.
-
D.
Malus
Malus is a genus of deciduous trees and shrubs in the rose family best known for cultivated apples and ornamental crabapples.
-
E.
Elm
Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
- 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_69a8864711648190b07bed24ed76258e |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb266dcb4819090be83556615d0db |
completed | March 7, 2026, 5:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3ef29e0819081b37664224dee91 |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.