Triple
T6376642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Mathematics, University of Waterloo |
E143482
|
entity |
| Predicate | affiliatedDepartment |
P589
|
FINISHED |
| Object |
Department of Combinatorics and Optimization, University of Waterloo
The Department of Combinatorics and Optimization at the University of Waterloo is a leading academic unit specializing in discrete mathematics, optimization theory, and related areas of theoretical and applied mathematics.
|
E588705
|
NE FINISHED |
How this triple was built (4 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: Department of Combinatorics and Optimization, University of Waterloo | Statement: [Faculty of Mathematics, University of Waterloo, affiliatedDepartment, Department of Combinatorics and Optimization, University of Waterloo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Department of Combinatorics and Optimization, University of Waterloo Context triple: [Faculty of Mathematics, University of Waterloo, affiliatedDepartment, Department of Combinatorics and Optimization, University of Waterloo]
-
A.
University of Toronto Department of Mathematics
The University of Toronto Department of Mathematics is a leading academic department renowned for its research and teaching in pure and applied mathematics, with notable faculty such as complexity theorist Stephen Cook.
-
B.
Department of Mathematics and Statistics (McGill University)
The Department of Mathematics and Statistics at McGill University is an academic unit renowned for research and teaching in pure and applied mathematics, statistics, and related fields.
-
C.
Department of Mathematics (University of British Columbia)
The Department of Mathematics at the University of British Columbia is a major academic unit known for its research and teaching in pure and applied mathematics at undergraduate and graduate levels.
-
D.
Department of Mathematics and Statistics (York University)
The Department of Mathematics and Statistics at York University is an academic unit offering undergraduate and graduate programs and conducting research in pure and applied mathematics, statistics, and related fields.
-
E.
Department of Computing Science, University of Alberta
The Department of Computing Science at the University of Alberta is a leading academic unit known for its research and teaching in computer science, including areas such as artificial intelligence, machine learning, and software systems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Department of Combinatorics and Optimization, University of Waterloo Triple: [Faculty of Mathematics, University of Waterloo, affiliatedDepartment, Department of Combinatorics and Optimization, University of Waterloo]
Generated description
The Department of Combinatorics and Optimization at the University of Waterloo is a leading academic unit specializing in discrete mathematics, optimization theory, and related areas of theoretical and applied mathematics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Department of Combinatorics and Optimization, University of Waterloo Target entity description: The Department of Combinatorics and Optimization at the University of Waterloo is a leading academic unit specializing in discrete mathematics, optimization theory, and related areas of theoretical and applied mathematics.
-
A.
University of Toronto Department of Mathematics
The University of Toronto Department of Mathematics is a leading academic department renowned for its research and teaching in pure and applied mathematics, with notable faculty such as complexity theorist Stephen Cook.
-
B.
Department of Mathematics and Statistics (McGill University)
The Department of Mathematics and Statistics at McGill University is an academic unit renowned for research and teaching in pure and applied mathematics, statistics, and related fields.
-
C.
Department of Mathematics (University of British Columbia)
The Department of Mathematics at the University of British Columbia is a major academic unit known for its research and teaching in pure and applied mathematics at undergraduate and graduate levels.
-
D.
Department of Mathematics and Statistics (York University)
The Department of Mathematics and Statistics at York University is an academic unit offering undergraduate and graduate programs and conducting research in pure and applied mathematics, statistics, and related fields.
-
E.
Department of Computing Science, University of Alberta
The Department of Computing Science at the University of Alberta is a leading academic unit known for its research and teaching in computer science, including areas such as artificial intelligence, machine learning, and software systems.
- F. None of above. chosen
Provenance (5 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0683d7af881908d66d5230e1bfcb6 |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d9dd9dc8190b2aca25feda3e690 |
completed | March 27, 2026, 7:11 a.m. |
| NEDg | Description generation | batch_69c62fb982088190ab4ccbd5ff23740d |
completed | March 27, 2026, 7:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6302e2f008190bd7ccdfbcddb3c07 |
completed | March 27, 2026, 7:22 a.m. |
Created at: March 22, 2026, 4:33 p.m.