Triple
T12897037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canisius University |
E308520
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Buffalo |
E22106
|
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: Buffalo | Statement: [Canisius University, city, Buffalo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buffalo Context triple: [Canisius University, city, Buffalo]
-
A.
Buffalo
chosen
Buffalo is a major city in western New York State known for its industrial history, proximity to Niagara Falls, and namesake Buffalo-style chicken wings.
-
B.
Rochester
Rochester is a historic cathedral city and former market town in Kent, England, known for its Norman castle, Romanesque cathedral, and strong associations with the novelist Charles Dickens.
-
C.
Rochester
Rochester is a small village located in Lorain County in the U.S. state of Ohio.
-
D.
Rochester
Rochester is a rural town in northern Victoria, Australia, known for its agricultural community and location near the Campaspe River.
-
E.
Rochester
Rochester is a major city in western New York State known historically for its role in industry, photography, and social reform movements.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9717d859481908957510babac2d69 |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8cec96c819089d253162bc4705a |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 9, 2026, 5:40 p.m.