Triple
T13434872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Queer Cinema |
E320202
|
entity |
| Predicate | hasNotableDirector |
P4744
|
FINISHED |
| Object |
Tom Kalin
Tom Kalin is an American filmmaker and screenwriter known for his pioneering work in New Queer Cinema, particularly the influential films "Swoon" and "Savage Grace."
|
E1041667
|
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: Tom Kalin | Statement: [New Queer Cinema, hasNotableDirector, Tom Kalin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Kalin Context triple: [New Queer Cinema, hasNotableDirector, Tom Kalin]
-
A.
Michael Nolin
Michael Nolin is an American film producer best known for his work on the acclaimed music drama "Mr. Holland's Opus."
-
B.
Tom Jeter
Tom Jeter is a fictional sketch-comedy performer and writer from the television series "Studio 60 on the Sunset Strip."
-
C.
Greg DePaul
Greg DePaul is an American screenwriter and playwright best known for co-writing the romantic comedy film "Bride Wars."
-
D.
Bob Cmelik
Bob Cmelik is a computer engineer best known as one of the founders of the innovative microprocessor company Transmeta.
-
E.
Charlie Kelmeckis
Charlie Kelmeckis is the introspective teenage protagonist and narrator of Stephen Chbosky’s coming-of-age novel and film "The Perks of Being a Wallflower."
- 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: Tom Kalin Triple: [New Queer Cinema, hasNotableDirector, Tom Kalin]
Generated description
Tom Kalin is an American filmmaker and screenwriter known for his pioneering work in New Queer Cinema, particularly the influential films "Swoon" and "Savage Grace."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Kalin Target entity description: Tom Kalin is an American filmmaker and screenwriter known for his pioneering work in New Queer Cinema, particularly the influential films "Swoon" and "Savage Grace."
-
A.
Michael Nolin
Michael Nolin is an American film producer best known for his work on the acclaimed music drama "Mr. Holland's Opus."
-
B.
Tom Jeter
Tom Jeter is a fictional sketch-comedy performer and writer from the television series "Studio 60 on the Sunset Strip."
-
C.
Greg DePaul
Greg DePaul is an American screenwriter and playwright best known for co-writing the romantic comedy film "Bride Wars."
-
D.
Bob Cmelik
Bob Cmelik is a computer engineer best known as one of the founders of the innovative microprocessor company Transmeta.
-
E.
Charlie Kelmeckis
Charlie Kelmeckis is the introspective teenage protagonist and narrator of Stephen Chbosky’s coming-of-age novel and film "The Perks of Being a Wallflower."
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaee29fec81908b07b4fca2922242 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f739902d148190ac14ac66f1f9512f |
completed | May 3, 2026, 12:03 p.m. |
| NEDg | Description generation | batch_69f73d6051e48190a39e8de98bfb839a |
completed | May 3, 2026, 12:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7411fbb9481908f0106b01f2583bf |
completed | May 3, 2026, 12:35 p.m. |
Created at: April 9, 2026, 9:40 p.m.