Triple
T21683411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayor of Edmonton |
E535167
|
entity |
| Predicate | officeHolder |
P537
|
FINISHED |
| Object | Amarjeet Sohi |
—
|
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: Amarjeet Sohi | Statement: [Mayor of Edmonton, officeHolder, Amarjeet Sohi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amarjeet Sohi Context triple: [Mayor of Edmonton, officeHolder, Amarjeet Sohi]
-
A.
Amarjeet Sohi
chosen
Amarjeet Sohi is a Canadian politician who serves as the mayor of Edmonton and is a former federal cabinet minister.
-
B.
Asheem Chandna
Asheem Chandna is a prominent venture capitalist known for investing in and advising leading enterprise technology and cybersecurity startups.
-
C.
Jassa Singh Ahluwalia
Jassa Singh Ahluwalia was an 18th-century Sikh military leader and statesman who played a key role in uniting Sikh misls and resisting Afghan and Mughal invasions in Punjab.
-
D.
Inderjit Dhillon
Inderjit Dhillon is a computer scientist known for his contributions to machine learning, numerical linear algebra, and data mining, and for serving as a professor at the University of Texas at Austin.
-
E.
Sanjiv Singh
Sanjiv Singh is a robotics researcher and professor known for his work in autonomous systems and field robotics at Carnegie Mellon University.
- 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_69e0c469b6ec8190aee4cadd1527db91 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef96c943888190acd783ce8677087c |
completed | April 27, 2026, 5:03 p.m. |
Created at: April 16, 2026, 6:43 p.m.