Triple
T3785099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Opposite of Sex |
E85511
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Michael Besman
Michael Besman is a film producer best known for his work on independent and character-driven movies in Hollywood.
|
E389680
|
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: Michael Besman | Statement: [The Opposite of Sex, producer, Michael Besman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Besman Context triple: [The Opposite of Sex, producer, Michael Besman]
-
A.
Philip Zhmachenko
Philip Zhmachenko was a Soviet military commander and general who played a significant leadership role in Red Army operations during World War II.
-
B.
Martin Lev
Martin Lev was a child actor best known for his role in the 1976 musical gangster film "Bugsy Malone."
-
C.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
-
D.
Matvey Manizer
Matvey Manizer was a prominent Soviet sculptor known for his monumental realist works and major public commissions across the USSR.
-
E.
Alec Miloslavsky
Alec Miloslavsky is a technology entrepreneur best known as a co-founder of the customer experience and contact center software company Genesys.
- 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: Michael Besman Triple: [The Opposite of Sex, producer, Michael Besman]
Generated description
Michael Besman is a film producer best known for his work on independent and character-driven movies in Hollywood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Besman Target entity description: Michael Besman is a film producer best known for his work on independent and character-driven movies in Hollywood.
-
A.
Philip Zhmachenko
Philip Zhmachenko was a Soviet military commander and general who played a significant leadership role in Red Army operations during World War II.
-
B.
Martin Lev
Martin Lev was a child actor best known for his role in the 1976 musical gangster film "Bugsy Malone."
-
C.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
-
D.
Matvey Manizer
Matvey Manizer was a prominent Soviet sculptor known for his monumental realist works and major public commissions across the USSR.
-
E.
Alec Miloslavsky
Alec Miloslavsky is a technology entrepreneur best known as a co-founder of the customer experience and contact center software company Genesys.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee3dd80f08190a1704521a764e22c |
completed | March 9, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb1c90648190a76cee07508a83b9 |
completed | March 14, 2026, 6:07 a.m. |
| NEDg | Description generation | batch_69b4fbacc8dc8190bb7e4a14e2d2f504 |
completed | March 14, 2026, 6:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4fc1f679881908e63b22d910c532b |
completed | March 14, 2026, 6:11 a.m. |
Created at: March 9, 2026, 3:13 p.m.