Triple
T6954915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pain & Gain |
E161216
|
entity |
| Predicate | notableCharacter |
P1481
|
FINISHED |
| Object | Paul Doyle |
E266044
|
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: Paul Doyle | Statement: [Pain & Gain, notableCharacter, Paul Doyle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Doyle Context triple: [Pain & Gain, notableCharacter, Paul Doyle]
-
A.
Patrick Doherty
Patrick Doherty was one of the unarmed civil rights marchers shot and killed by British soldiers during the Bloody Sunday massacre in Derry, Northern Ireland, in 1972.
-
B.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
C.
Paul McDermott
Paul McDermott is an Australian comedian, writer, television host, and singer best known for his work with the comedy group The Doug Anthony All Stars and as a longtime host on the satirical news quiz show "Good News Week."
-
D.
Chris Donlon
Chris Donlon is a film editor known for his work on the feature film "Kicks."
-
E.
Kevin Corrigan
chosen
Kevin Corrigan is an American character actor known for his offbeat, often darkly comic supporting roles in numerous independent films and major studio movies.
- 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dace1a94819095311e4288f01784 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7753f1fe08190a734e371db4d1d38 |
completed | March 28, 2026, 6:29 a.m. |
Created at: March 27, 2026, 2:29 p.m.