Triple
T22769507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bandersnatch |
E563215
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Fionn Whitehead |
—
|
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: Fionn Whitehead | Statement: [Bandersnatch, portrayedBy, Fionn Whitehead]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fionn Whitehead Context triple: [Bandersnatch, portrayedBy, Fionn Whitehead]
-
A.
Fionn Whitehead
chosen
Fionn Whitehead is a British actor best known for his breakout leading role in Christopher Nolan’s World War II film "Dunkirk."
-
B.
Rupert Friend
Rupert Friend is an English actor known for roles in films like "Pride & Prejudice" and the TV series "Homeland."
-
C.
Thomas Brodie-Sangster
Thomas Brodie-Sangster is an English actor known for his roles in projects such as "Nanny McPhee," "The Maze Runner" series, and "Game of Thrones."
-
D.
Noah Jupe
Noah Jupe is a British actor known for his acclaimed performances as a child and teen in films such as "A Quiet Place," "Honey Boy," and "Ford v Ferrari."
-
E.
Nicholas Hoult
Nicholas Hoult is an English actor known for his versatile roles in films such as "About a Boy," the "X-Men" series, and "Mad Max: Fury Road," as well as the TV series "Skins" and "The Great."
- 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_69e24552e11c81909c2d61578a558bd7 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17b59c9cc8190a6edd68f3209a672 |
completed | April 29, 2026, 3:30 a.m. |
Created at: April 17, 2026, 3:27 p.m.