Triple
T20495258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Healy |
E502854
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Michael Harney |
—
|
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: Michael Harney | Statement: [Sam Healy, portrayedBy, Michael Harney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Harney Context triple: [Sam Healy, portrayedBy, Michael Harney]
-
A.
Michael Harney
chosen
Michael Harney is an American character actor best known for his roles in television series such as "Orange Is the New Black" and numerous crime and drama shows.
-
B.
Christopher Harter
Christopher Harter is known primarily as the husband of British actor Jeremy Kemp.
-
C.
Ben Harney
Ben Harney is an American actor best known for his Tony Award–winning performance in the original Broadway production of the musical "Dreamgirls."
-
D.
John Harron
John Harron was an American film actor of the silent and early sound era, known for appearing in numerous supporting roles during the 1920s and 1930s.
-
E.
Michael Harnett
Michael Harnett is the birth name of Michael Hartnett, a prominent Irish poet known for his lyrical work in both English and Irish.
- 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_69e0b4b0373881909dd3e9387f82eab4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cbd2dfc81908204f7bfa8a763b6 |
completed | April 20, 2026, 9:38 p.m. |
Created at: April 16, 2026, 11:35 a.m.