Triple
T55396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reser Stadium |
E1094
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Al Reser
Al Reser was an American businessman and Oregon State University alumnus best known as the longtime head of Reser's Fine Foods and a major benefactor of OSU athletics.
|
E12318
|
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: Al Reser | Statement: [Reser Stadium, namedAfter, Al Reser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Al Reser Context triple: [Reser Stadium, namedAfter, Al Reser]
-
A.
Edwin
Edwin is a masculine given name of Old English origin meaning "rich friend" or "prosperous friend."
-
B.
Robnett
Robnett is a given middle name that appears in the full name of individuals such as the American politician and jurist James Lick Robnett.
-
C.
Peter Amundson
Peter Amundson is a film editor best known for his work on major Hollywood productions, including the science-fiction action film "Pacific Rim."
-
D.
MacDouglas
MacDouglas is a Scottish surname variant of Douglas, traditionally associated with clans and families of Scottish heritage.
-
E.
Harold Hazen
Harold Hazen was an American electrical engineer and MIT professor known for his pioneering work in control systems and his role in developing early analog computing devices.
- 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: Al Reser Triple: [Reser Stadium, namedAfter, Al Reser]
Generated description
Al Reser was an American businessman and Oregon State University alumnus best known as the longtime head of Reser's Fine Foods and a major benefactor of OSU athletics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Al Reser Target entity description: Al Reser was an American businessman and Oregon State University alumnus best known as the longtime head of Reser's Fine Foods and a major benefactor of OSU athletics.
-
A.
Edwin
Edwin is a masculine given name of Old English origin meaning "rich friend" or "prosperous friend."
-
B.
Robnett
Robnett is a given middle name that appears in the full name of individuals such as the American politician and jurist James Lick Robnett.
-
C.
Peter Amundson
Peter Amundson is a film editor best known for his work on major Hollywood productions, including the science-fiction action film "Pacific Rim."
-
D.
MacDouglas
MacDouglas is a Scottish surname variant of Douglas, traditionally associated with clans and families of Scottish heritage.
-
E.
Harold Hazen
Harold Hazen was an American electrical engineer and MIT professor known for his pioneering work in control systems and his role in developing early analog computing devices.
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b06c5488190afb5429a7999e3f3 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a284f9fcd48190a3331f06d5dc00e8 |
completed | Feb. 28, 2026, 6:02 a.m. |
| NEDg | Description generation | batch_69a28652e5b081908010cf3910cea87f |
completed | Feb. 28, 2026, 6:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a286f855608190b876a71dc26e0624 |
completed | Feb. 28, 2026, 6:11 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.