Triple
T20883310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thirteen Lives |
E514206
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Colin Farrell |
—
|
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: Colin Farrell | Statement: [Thirteen Lives, stars, Colin Farrell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colin Farrell Context triple: [Thirteen Lives, stars, Colin Farrell]
-
A.
Colin Farrell
chosen
Colin Farrell is an Irish actor known for his versatile performances in films such as "In Bruges," "Phone Booth," and "The Banshees of Inisherin."
-
B.
Nicholas Farrell
Nicholas Farrell is a British actor known for his work in film, television, and theatre, including prominent roles in productions such as Kenneth Branagh’s "Hamlet" and the historical drama "Chariots of Fire."
-
C.
Brendan Gleeson
Brendan Gleeson is an acclaimed Irish actor known for his powerful character roles in films such as In Bruges, The Guard, and the Harry Potter series.
-
D.
Seamus Tierney
Seamus Tierney is a cinematographer known for his work on the film "Love, Antosha."
-
E.
Kevin Meaney
Kevin Meaney was an American stand-up comedian and actor known for his offbeat, family-themed humor and appearances on shows like "The Tonight Show" and the sitcom "Uncle Buck."
- 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.