Triple
T13672931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Café Society |
E327796
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Ben Dorfman
Ben Dorfman is a fictional character in Woody Allen’s 2016 romantic comedy film "Café Society."
|
E1154451
|
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: Ben Dorfman | Statement: [Café Society, character, Ben Dorfman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ben Dorfman Context triple: [Café Society, character, Ben Dorfman]
-
A.
Steven Fierberg
Steven Fierberg is an American cinematographer known for his work on feature films and television series, including the romantic drama "Love & Other Drugs."
-
B.
Eric Tannenbaum
Eric Tannenbaum is a television producer best known for his work on popular American sitcoms, including serving as an executive producer on "Two and a Half Men."
-
C.
Dov Frohman
Dov Frohman is an Israeli engineer and inventor best known for pioneering the EPROM (erasable programmable read-only memory) and for his leadership role at Intel Israel.
-
D.
Guy Rothblum
Guy Rothblum is a theoretical computer scientist known for his work in cryptography and complexity theory.
-
E.
Avron Fogelman
Avron Fogelman is an American businessman and philanthropist best known as a former co-owner of the Kansas City Royals Major League Baseball team.
- 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: Ben Dorfman Triple: [Café Society, character, Ben Dorfman]
Generated description
Ben Dorfman is a fictional character in Woody Allen’s 2016 romantic comedy film "Café Society."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ben Dorfman Target entity description: Ben Dorfman is a fictional character in Woody Allen’s 2016 romantic comedy film "Café Society."
-
A.
Steven Fierberg
Steven Fierberg is an American cinematographer known for his work on feature films and television series, including the romantic drama "Love & Other Drugs."
-
B.
Eric Tannenbaum
Eric Tannenbaum is a television producer best known for his work on popular American sitcoms, including serving as an executive producer on "Two and a Half Men."
-
C.
Dov Frohman
Dov Frohman is an Israeli engineer and inventor best known for pioneering the EPROM (erasable programmable read-only memory) and for his leadership role at Intel Israel.
-
D.
Guy Rothblum
Guy Rothblum is a theoretical computer scientist known for his work in cryptography and complexity theory.
-
E.
Avron Fogelman
Avron Fogelman is an American businessman and philanthropist best known as a former co-owner of the Kansas City Royals Major League Baseball team.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65aab348190a6611f5765f8392d |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1330527481908d518093debc9ad1 |
completed | May 9, 2026, 10:57 a.m. |
| NEDg | Description generation | batch_69ff142e99e081909d01cac0416f1bde |
completed | May 9, 2026, 11:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff14c61eb08190ba854b541eb1ce14 |
completed | May 9, 2026, 11:04 a.m. |
Created at: April 9, 2026, 9:53 p.m.