Triple
T10188968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | You Can Count on Me |
E237982
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object |
Stephen Kazmierski
Stephen Kazmierski is a cinematographer best known for his work on the acclaimed independent film "You Can Count on Me."
|
E849597
|
NE FINISHED |
How this triple was built (4 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: Stephen Kazmierski | Statement: [You Can Count on Me, cinematography, Stephen Kazmierski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stephen Kazmierski Context triple: [You Can Count on Me, cinematography, Stephen Kazmierski]
-
A.
Kevin Sierzega
Kevin Sierzega is a musician best known as a member of the punk rock band Squirtgun.
-
B.
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
C.
Sam Kiszka
Sam Kiszka is an American bassist and keyboardist best known as a founding member of the rock band Greta Van Fleet.
-
D.
Peter Jankowski
Peter Jankowski is a television producer best known for his longtime work on Dick Wolf’s crime drama franchises, including the Chicago and Law & Order series.
-
E.
Jeff Jagodzinski
Jeff Jagodzinski is an American football coach best known for his tenure as head coach at Boston College and his extensive experience as an offensive coach in both college football and the NFL.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Stephen Kazmierski Triple: [You Can Count on Me, cinematography, Stephen Kazmierski]
Generated description
Stephen Kazmierski is a cinematographer best known for his work on the acclaimed independent film "You Can Count on Me."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stephen Kazmierski Target entity description: Stephen Kazmierski is a cinematographer best known for his work on the acclaimed independent film "You Can Count on Me."
-
A.
Kevin Sierzega
Kevin Sierzega is a musician best known as a member of the punk rock band Squirtgun.
-
B.
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
C.
Sam Kiszka
Sam Kiszka is an American bassist and keyboardist best known as a founding member of the rock band Greta Van Fleet.
-
D.
Peter Jankowski
Peter Jankowski is a television producer best known for his longtime work on Dick Wolf’s crime drama franchises, including the Chicago and Law & Order series.
-
E.
Jeff Jagodzinski
Jeff Jagodzinski is an American football coach best known for his tenure as head coach at Boston College and his extensive experience as an offensive coach in both college football and the NFL.
- F. None of above. chosen
Provenance (5 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded7c3278819093312665b54d888c |
completed | April 2, 2026, 4:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d652a5fa6c8190ac153678f979cb74 |
completed | April 8, 2026, 1:05 p.m. |
| NEDg | Description generation | batch_69d656c057348190823ae5f1ecc33b61 |
completed | April 8, 2026, 1:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6575318a08190b4591b57f2595d51 |
completed | April 8, 2026, 1:25 p.m. |
Created at: March 30, 2026, 9:12 p.m.