Triple
T20753032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meadowland |
E510779
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Chris Rossi
Chris Rossi is a film and television writer best known for his work on the drama feature "Meadowland."
|
E1449608
|
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: Chris Rossi | Statement: [Meadowland, screenwriter, Chris Rossi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Rossi Context triple: [Meadowland, screenwriter, Chris Rossi]
-
A.
Michael Rossi
Michael Rossi is a central fictional character in the "Peyton Place" franchise, often portrayed as a principled, compassionate professional entangled in the small town’s complex social and moral dramas.
-
B.
Leo Rossi
Leo Rossi is an American character actor known for his supporting roles in crime dramas and thrillers in film and television.
-
C.
Eric Ragno
Eric Ragno is a rock keyboardist known for his work with melodic hard rock and metal bands, including serving as a member of Trixter.
-
D.
Dan Iassogna
Dan Iassogna is a veteran Major League Baseball umpire who has officiated numerous postseason games, including serving as crew chief in the World Series.
-
E.
Andy Robustelli
Andy Robustelli was a Hall of Fame defensive end best known for his dominant play with the New York Giants during the 1950s and early 1960s.
- 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: Chris Rossi Triple: [Meadowland, screenwriter, Chris Rossi]
Generated description
Chris Rossi is a film and television writer best known for his work on the drama feature "Meadowland."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chris Rossi Target entity description: Chris Rossi is a film and television writer best known for his work on the drama feature "Meadowland."
-
A.
Michael Rossi
Michael Rossi is a central fictional character in the "Peyton Place" franchise, often portrayed as a principled, compassionate professional entangled in the small town’s complex social and moral dramas.
-
B.
Leo Rossi
Leo Rossi is an American character actor known for his supporting roles in crime dramas and thrillers in film and television.
-
C.
Eric Ragno
Eric Ragno is a rock keyboardist known for his work with melodic hard rock and metal bands, including serving as a member of Trixter.
-
D.
Dan Iassogna
Dan Iassogna is a veteran Major League Baseball umpire who has officiated numerous postseason games, including serving as crew chief in the World Series.
-
E.
Andy Robustelli
Andy Robustelli was a Hall of Fame defensive end best known for his dominant play with the New York Giants during the 1950s and early 1960s.
- 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_69e0b4c909ec8190b05987f1639513f6 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c22be6588190b137193cb3184fc0 |
completed | April 21, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08ef82cb788190b8b4a7cf71265ba3 |
completed | May 16, 2026, 10:28 p.m. |
| NEDg | Description generation | batch_6a08f1c8a1b08190b8d0f3e84a195ef5 |
completed | May 16, 2026, 10:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08f22a69348190869d69ec6144f85e |
completed | May 16, 2026, 10:39 p.m. |
Created at: April 16, 2026, 12:34 p.m.