Triple
T13610362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Smile Back |
E325171
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object |
Adam Salky
Adam Salky is an American film director known for his work on independent dramas exploring complex emotional and psychological themes.
|
E1052834
|
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: Adam Salky | Statement: [I Smile Back, director, Adam Salky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adam Salky Context triple: [I Smile Back, director, Adam Salky]
-
A.
Philip Chosky
Philip Chosky was a philanthropist and benefactor known for his significant support of the arts, particularly theater.
-
B.
Matthew Salsberg
Matthew Salsberg is a television writer and producer best known for his work on the dark comedy series "Weeds."
-
C.
Glen Sobel
Glen Sobel is an American rock drummer best known for his work with Alice Cooper and various high-profile hard rock and metal acts.
-
D.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
E.
Michael Sokolove
Michael Sokolove is an American journalist and nonfiction author known for his in-depth sports and cultural reporting and narrative books.
- 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: Adam Salky Triple: [I Smile Back, director, Adam Salky]
Generated description
Adam Salky is an American film director known for his work on independent dramas exploring complex emotional and psychological themes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Adam Salky Target entity description: Adam Salky is an American film director known for his work on independent dramas exploring complex emotional and psychological themes.
-
A.
Philip Chosky
Philip Chosky was a philanthropist and benefactor known for his significant support of the arts, particularly theater.
-
B.
Matthew Salsberg
Matthew Salsberg is a television writer and producer best known for his work on the dark comedy series "Weeds."
-
C.
Glen Sobel
Glen Sobel is an American rock drummer best known for his work with Alice Cooper and various high-profile hard rock and metal acts.
-
D.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
E.
Michael Sokolove
Michael Sokolove is an American journalist and nonfiction author known for his in-depth sports and cultural reporting and narrative books.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0aa9a1481908c6f92495aff86c6 |
completed | April 12, 2026, 2:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78ae56e2081909c0fd044ce3730a9 |
completed | May 3, 2026, 5:50 p.m. |
| NEDg | Description generation | batch_69f78c8d68f081909f5e6b8ab05a3ce2 |
completed | May 3, 2026, 5:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f78d6d74bc8190ad5476a06e8fd8ad |
completed | May 3, 2026, 6:01 p.m. |
Created at: April 9, 2026, 9:50 p.m.