Triple
T21113076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carry On |
E520220
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Peter Rogers |
—
|
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: Peter Rogers | Statement: [Carry On, creator, Peter Rogers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Rogers Context triple: [Carry On, creator, Peter Rogers]
-
A.
Peter Rogers
chosen
Peter Rogers was a British film producer best known for overseeing the long-running and popular "Carry On" comedy film series.
-
B.
Paul Rogers
Paul Rogers is an American film editor best known for his Academy Award–winning work on the multiverse film "Everything Everywhere All at Once."
-
C.
Graham Rogers
Graham Rogers is an American actor known for his roles in television series such as "The Kominsky Method," "Quantico," and "Atypical."
-
D.
Ben Rogers
Ben Rogers was the husband of Mary Kay Ash, the famed American businesswoman and founder of Mary Kay Cosmetics.
-
E.
Ben Rogers
Ben Rogers is a lively, boastful boy in Mark Twain’s "The Adventures of Tom Sawyer," known for being one of Tom’s close companions in their childhood adventures.
- 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_69e0b509a318819092fbbcb21d1fe603 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72103b3888190a19e9a40f01fb439 |
completed | April 21, 2026, 7:02 a.m. |
Created at: April 16, 2026, 2:54 p.m.