Triple
T13701742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Power Book IV: Force |
E328534
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Joseph Sikora |
E1066010
|
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: Joseph Sikora | Statement: [Power Book IV: Force, castMember, Joseph Sikora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph Sikora Context triple: [Power Book IV: Force, castMember, Joseph Sikora]
-
A.
Joseph Sikora
chosen
Joseph Sikora is an American actor best known for playing the ruthless drug dealer Tommy Egan in the Power television franchise.
-
B.
Joe Pisarcik
Joe Pisarcik is a former NFL quarterback best known for his infamous late-game fumble in 1978 that led to the "Miracle at the Meadowlands."
-
C.
Edward Kazmierczak
Edward Kazmierczak is a former Polish footballer best known for his time playing for Stal Mielec.
-
D.
Greg Joswiak
Greg Joswiak is a senior Apple executive who serves as the company’s Senior Vice President of Worldwide Marketing.
-
E.
John Sikela
John Sikela was a Golden Age comic book artist best known for his work on early Superman stories for DC Comics.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc879adc88190b03f1cf815b71061 |
completed | April 12, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c6fcddf08190bee3dce22be39a34 |
completed | May 3, 2026, 10:06 p.m. |
Created at: April 9, 2026, 9:54 p.m.