Triple
T10495365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boomers |
E247524
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | June Whitfield |
E864521
|
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: June Whitfield | Statement: [Boomers, stars, June Whitfield]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: June Whitfield Context triple: [Boomers, stars, June Whitfield]
-
A.
June Whitfield
chosen
June Whitfield was a renowned English comedy actress best known for her long career in British radio and television, including her role as the eccentric Mother/Gran in the sitcom "Absolutely Fabulous."
-
B.
Peter Hewitt
Peter Hewitt is a British film director known for helming family-oriented and fantasy comedies such as "Bill & Ted's Bogus Journey" and "Garfield: The Movie."
-
C.
Horace Gasquet
Horace Gasquet was a local figure in California, likely an early settler or landowner, after whom the community of Gasquet, California, was named.
-
D.
Tom Budge
Tom Budge is an Australian actor known for his character roles in film and television, including appearances in projects like "The Proposition" and "Gallipoli."
-
E.
Paul Groth
Paul Groth is a computer scientist known for his work in knowledge representation, semantic web technologies, and data provenance.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5098be488819083d614f528cd82fb |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcbafe9481908fb23cfdf150adac |
completed | April 10, 2026, 11:19 a.m. |
Created at: April 6, 2026, 12:24 p.m.