Triple
T20883312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thirteen Lives |
E514206
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Tom Bateman |
—
|
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: Tom Bateman | Statement: [Thirteen Lives, stars, Tom Bateman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Bateman Context triple: [Thirteen Lives, stars, Tom Bateman]
-
A.
Tom Bateman
chosen
Tom Bateman is a British actor known for his work in film, television, and theatre, including prominent roles in period dramas and crime mysteries.
-
B.
Nick Bateman
Nick Bateman is a Canadian actor and model known for his roles in romantic films and his large social media following.
-
C.
Alan Bateman
Alan Bateman was an Australian television producer and executive best known for creating the long-running soap opera "Home and Away."
-
D.
Kent Bateman
Kent Bateman is an American film and television producer, director, and actor, best known as the father of actor Jason Bateman and for his work in low-budget and independent productions.
-
E.
Sean Bateman
Sean Bateman is a fictional character from Bret Easton Ellis’s novels, notably "The Rules of Attraction," where he appears as a hedonistic, disaffected college student.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c67b03088190be7cbcde8c59509a |
completed | April 21, 2026, 12:36 a.m. |
Created at: April 16, 2026, 12:46 p.m.