Triple
T15368633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chopper |
E367483
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Kate Beahan |
E217858
|
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: Kate Beahan | Statement: [Chopper, starring, Kate Beahan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Beahan Context triple: [Chopper, starring, Kate Beahan]
-
A.
Kate Beahan
chosen
Kate Beahan is an Australian actress known for her roles in films such as "Flightplan" and "The Wicker Man."
-
B.
Kate Beck
Kate Beck is a fictional character, notably the protagonist of the "Kate Beck" mystery novel series by author Dianne Harman.
-
C.
Kate Hennessy
Kate Hennessy is an American writer and the granddaughter of Catholic social activist Dorothy Day, known for her memoirs and work chronicling her family’s legacy.
-
D.
Rebecca McGuinness
Rebecca McGuinness is known as the wife of renowned English motorcycle road racer John McGuinness.
-
E.
Kate Nelligan
Kate Nelligan is a Canadian actress acclaimed for her work in film, television, and theatre, noted for her intense dramatic performances and multiple award nominations.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4a7cdc8190b7b48c97e774c306 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbba9fb08190b800af317f0c9abf |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 3:18 a.m.