Triple
T12877441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Heat |
E308003
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Art Ridzik
Art Ridzik is a tough, wisecracking Chicago police detective portrayed by Jim Belushi in the 1988 action film "Red Heat."
|
E1006452
|
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: Art Ridzik | Statement: [Red Heat, mainCharacter, Art Ridzik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Art Ridzik Context triple: [Red Heat, mainCharacter, Art Ridzik]
-
A.
Michael Kuzak
Michael Kuzak is a central attorney character on the television legal drama "L.A. Law," known for his idealism and high-profile courtroom battles.
-
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.
Eric Dapkewicz
Eric Dapkewicz is a film editor best known for his work on major animated features, including DreamWorks Animation’s "Puss in Boots."
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
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: Art Ridzik Triple: [Red Heat, mainCharacter, Art Ridzik]
Generated description
Art Ridzik is a tough, wisecracking Chicago police detective portrayed by Jim Belushi in the 1988 action film "Red Heat."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Art Ridzik Target entity description: Art Ridzik is a tough, wisecracking Chicago police detective portrayed by Jim Belushi in the 1988 action film "Red Heat."
-
A.
Michael Kuzak
Michael Kuzak is a central attorney character on the television legal drama "L.A. Law," known for his idealism and high-profile courtroom battles.
-
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.
Eric Dapkewicz
Eric Dapkewicz is a film editor best known for his work on major animated features, including DreamWorks Animation’s "Puss in Boots."
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fa8474819086a8af3c90f3ca84 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69bb83bac8190838f7537b806317c |
completed | May 3, 2026, 12:50 a.m. |
| NEDg | Description generation | batch_69f69cc6fa84819093a4317ab355f62b |
completed | May 3, 2026, 12:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69d845a9081909b40562825c1c500 |
completed | May 3, 2026, 12:57 a.m. |
Created at: April 9, 2026, 5:38 p.m.