Triple
T4709937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Into the Blue |
E104483
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
David Zelon
David Zelon is a film producer best known for working on action and thriller movies, including the underwater adventure film "Into the Blue."
|
E469279
|
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: David Zelon | Statement: [Into the Blue, producer, David Zelon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Zelon Context triple: [Into the Blue, producer, David Zelon]
-
A.
David Zellner
David Zellner is an American independent filmmaker and actor known for his offbeat, character-driven films such as "Kumiko, the Treasure Hunter" and "Damsel."
-
B.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
-
C.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
-
D.
Len Blum
Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
-
E.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
- 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: David Zelon Triple: [Into the Blue, producer, David Zelon]
Generated description
David Zelon is a film producer best known for working on action and thriller movies, including the underwater adventure film "Into the Blue."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: David Zelon Target entity description: David Zelon is a film producer best known for working on action and thriller movies, including the underwater adventure film "Into the Blue."
-
A.
David Zellner
David Zellner is an American independent filmmaker and actor known for his offbeat, character-driven films such as "Kumiko, the Treasure Hunter" and "Damsel."
-
B.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
-
C.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
-
D.
Len Blum
Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
-
E.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
- 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_69bd43eac3c08190af7e4020c6c3704c |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd63ee712c81908da60aa0df58efe0 |
completed | March 20, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be43a16d1c819081e9e20f015c7f90 |
completed | March 21, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69be443674b081909a4fc17c6a087198 |
completed | March 21, 2026, 7:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be44e6f9108190bd27468aa966b60a |
completed | March 21, 2026, 7:12 a.m. |
Created at: March 20, 2026, 1:17 p.m.