Triple
T57845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nelson |
E1145
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Nelsen |
E12672
|
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: Nelsen | Statement: [Nelson, hasVariant, Nelsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nelsen Context triple: [Nelson, hasVariant, Nelsen]
-
A.
Nilsen
chosen
Nilsen is a surname, primarily of Scandinavian origin, that serves as a variant spelling of Nelson.
-
B.
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.
-
C.
Neal
Neal is a masculine given name of Gaelic origin, commonly used in English-speaking countries.
-
D.
Nggela Pile
Nggela Pile is one of the principal islands in the Florida Islands group of the Solomon Islands in the South Pacific.
-
E.
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."
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b1bf2c081908f20e13939b713ff |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2d72dfb1c81908e127aa79bee1bdc |
completed | Feb. 28, 2026, 11:53 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.