Triple
T8084300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murphy's Romance |
E188692
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Brian Kerwin |
E470963
|
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: Brian Kerwin | Statement: [Murphy's Romance, starring, Brian Kerwin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brian Kerwin Context triple: [Murphy's Romance, starring, Brian Kerwin]
-
A.
Brian Kerwin
chosen
Brian Kerwin is an American actor known for his work in film, television, and theater, including roles in movies like "King Kong Lives" and various popular TV series.
-
B.
Brian Warfield
Brian Warfield is an Irish musician and songwriter best known as a member of the folk band The Wolfe Tones.
-
C.
Kirk Stievely
Kirk Stievely is a British actor best known as the former husband of actress Victoria Tennant.
-
D.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
E.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
- 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb415e61ac81909e924aea69a7ff77 |
completed | March 31, 2026, 3:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce38eaf93481908939e770f0dda7f5 |
completed | April 2, 2026, 9:37 a.m. |
Created at: March 30, 2026, 5:29 p.m.