Triple
T22087995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Rudkin |
E545834
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Pepperidge Farm |
—
|
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: Pepperidge Farm | Statement: [Margaret Rudkin, employer, Pepperidge Farm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pepperidge Farm Context triple: [Margaret Rudkin, employer, Pepperidge Farm]
-
A.
Pepperidge Farm
chosen
Pepperidge Farm is an American food company best known for its cookies, crackers, and baked goods such as Milano cookies and Goldfish crackers.
-
B.
Pepperidge Farm Milano
Pepperidge Farm Milano is a popular line of distinctive sandwich cookies known for their crisp, oval-shaped biscuits and rich chocolate filling.
-
C.
Nabisco
Nabisco is a major American snack food company best known for iconic brands like Oreo cookies and Ritz crackers.
-
D.
Triscuit
Triscuit is a popular American brand of baked whole-grain wheat crackers known for their woven texture and simple ingredients.
-
E.
Hillshire Farm
Hillshire Farm is a popular American brand known for its packaged meats, including sausages, deli meats, and smoked meats.
- 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_69e11e36d03c8190a83a1ba802b7231b |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128e3a98481908a7b3dc3f2a90276 |
completed | April 28, 2026, 9:38 p.m. |
Created at: April 16, 2026, 8:29 p.m.