Triple
T20592152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nellie Oleson |
E505955
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Alison Arngrim |
—
|
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: Alison Arngrim | Statement: [Nellie Oleson, portrayedBy, Alison Arngrim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alison Arngrim Context triple: [Nellie Oleson, portrayedBy, Alison Arngrim]
-
A.
Alison Arngrim
chosen
Alison Arngrim is an American actress and author best known for her iconic portrayal of the scheming Nellie Oleson on the classic television series "Little House on the Prairie."
-
B.
Alison Owen
Alison Owen is a British film producer known for acclaimed works such as "Elizabeth," "Shaun of the Dead," and "Saving Mr. Banks."
-
C.
Alison Woods
Alison Woods is an American actress best known for her role in the horror-comedy film "Detention."
-
D.
Alison Marr
Alison Marr is a mathematician known for her work in combinatorics and for her contributions to mathematics education and outreach.
-
E.
Alison Gordon
Alison Gordon is a fictional character from the "Saw" horror film franchise, known as the wife of Dr. Lawrence Gordon and a victim in Jigsaw’s deadly games.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a97c10f081909daf635e9be0dd22 |
completed | April 20, 2026, 10:32 p.m. |
Created at: April 16, 2026, 11:40 a.m.