Triple
T11050240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Encino Man |
E261225
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
George Zaloom
George Zaloom is a film producer best known for his work on the 1992 comedy movie "Encino Man."
|
E901345
|
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: George Zaloom | Statement: [Encino Man, producer, George Zaloom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Zaloom Context triple: [Encino Man, producer, George Zaloom]
-
A.
Don Zimmerman
Don Zimmerman is a film editor known for his work on major Hollywood movies, including the family adventure-comedy "Night at the Museum."
-
B.
Fred Zollner
Fred Zollner was an American industrialist and basketball executive best known as the longtime owner of the Pistons franchise and a key figure in the early development of the NBA.
-
C.
Spike Feresten
Spike Feresten is an American television writer, producer, and talk show host best known for his work on Seinfeld and his own late-night series Talkshow with Spike Feresten.
-
D.
George Zames
George Zames was a prominent control theorist known for his foundational contributions to robust control and H-infinity methods in systems engineering.
-
E.
Paul Menzel
Paul Menzel is a relatively obscure individual whose specific public notability is not clearly established from the given information.
- 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: George Zaloom Triple: [Encino Man, producer, George Zaloom]
Generated description
George Zaloom is a film producer best known for his work on the 1992 comedy movie "Encino Man."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: George Zaloom Target entity description: George Zaloom is a film producer best known for his work on the 1992 comedy movie "Encino Man."
-
A.
Don Zimmerman
Don Zimmerman is a film editor known for his work on major Hollywood movies, including the family adventure-comedy "Night at the Museum."
-
B.
Fred Zollner
Fred Zollner was an American industrialist and basketball executive best known as the longtime owner of the Pistons franchise and a key figure in the early development of the NBA.
-
C.
Spike Feresten
Spike Feresten is an American television writer, producer, and talk show host best known for his work on Seinfeld and his own late-night series Talkshow with Spike Feresten.
-
D.
George Zames
George Zames was a prominent control theorist known for his foundational contributions to robust control and H-infinity methods in systems engineering.
-
E.
Paul Menzel
Paul Menzel is a relatively obscure individual whose specific public notability is not clearly established from the given information.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79868c78881908c8e3672c05ae7ec |
completed | April 9, 2026, 12:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3aa146b148190a87205e542cc718f |
completed | April 18, 2026, 3:58 p.m. |
| NEDg | Description generation | batch_69e3ad0379888190b2f56d36d79bf97d |
completed | April 18, 2026, 4:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b206c7a4819087eb06faa6e1af21 |
completed | April 18, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:26 p.m.