Triple
T17223955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danny Noonan |
E418061
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Michael O'Keefe |
E377599
|
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: Michael O'Keefe | Statement: [Danny Noonan, portrayedBy, Michael O'Keefe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael O'Keefe Context triple: [Danny Noonan, portrayedBy, Michael O'Keefe]
-
A.
Michael O’Keefe
chosen
Michael O’Keefe is an American actor best known for his role as young caddie Danny Noonan in the classic comedy film "Caddyshack."
-
B.
Tom Kilpatrick
Tom Kilpatrick was a screenwriter best known for his work on the 1940 science fiction horror film "Dr. Cyclops."
-
C.
Charles Keefe
Charles Keefe is a fictional high-profile political figure whose life becomes the focus of an assassination plot in the thriller film "Red Eye."
-
D.
Tom Ryan
Tom Ryan is the central protagonist of the television series "The Unit," depicted as a highly skilled and seasoned leader of an elite U.S. Army special operations team.
-
E.
Don Logan
Don Logan is a volatile and menacing criminal character from the British film "Sexy Beast," best known for Ben Kingsley’s intense, Oscar-nominated performance.
- 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42ddfe3bc8190b22cee4fc0590b74 |
completed | April 19, 2026, 1:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0167596ab481909df59ce68c7f640e |
completed | May 11, 2026, 5:21 a.m. |
Created at: April 10, 2026, 5:38 a.m.