Triple
T17651255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Elizabeth II Highway |
E429494
|
entity |
| Predicate | connectsCommunity |
P12608
|
FINISHED |
| Object | Olds |
—
|
NE NERFINISHED |
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: Olds | Statement: [Queen Elizabeth II Highway, connectsCommunity, Olds]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Olds Context triple: [Queen Elizabeth II Highway, connectsCommunity, Olds]
-
A.
Olds
Olds is a surname most notably associated with American automobile industry pioneer Ransom E. Olds, founder of Oldsmobile and REO Motor Car Company.
-
B.
Olds
chosen
Olds is a small agricultural and educational hub in central Alberta, Canada, known for Olds College and its surrounding farming community.
-
C.
Olds Motor Vehicle Company
Olds Motor Vehicle Company was an early American automobile manufacturer that became one of the first mass producers of cars in the United States.
-
D.
Bonger
Bonger is a Dutch surname most notably associated with Johanna van Gogh-Bonger, the key figure in preserving and promoting Vincent van Gogh’s artistic legacy.
-
E.
Thorndike
Thorndike is a surname most notably associated with Edward L. Thorndike, an influential American psychologist known for his work on learning theory and the law of effect.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46e3d4948819084de72bed922be6e |
completed | April 19, 2026, 5:55 a.m. |
Created at: April 10, 2026, 6:05 a.m.