Triple
T10494476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garden State |
E247498
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Richard Klubeck
Richard Klubeck is a film producer best known for his work on the acclaimed independent movie "Garden State."
|
E899882
|
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: Richard Klubeck | Statement: [Garden State, producer, Richard Klubeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richard Klubeck Context triple: [Garden State, producer, Richard Klubeck]
-
A.
Richard Dybeck
Richard Dybeck was a 19th-century Swedish jurist, antiquarian, and poet best known for writing the lyrics to Sweden’s de facto national anthem, "Du gamla, Du fria."
-
B.
Paul Biegler
Paul Biegler is a small-town Michigan lawyer and the central protagonist of the courtroom drama novel and film "Anatomy of a Murder."
-
C.
Richard Gottehrer
Richard Gottehrer is an American record producer, songwriter, and music executive known for co-founding Sire Records and working with numerous influential rock and pop artists.
-
D.
Fred Schuler
Fred Schuler is a cinematographer best known for his work on films such as the 1980 comedy "Stir Crazy."
-
E.
Michael Kozoll
Michael Kozoll is an American television writer and producer best known for co-creating the influential police drama series "Hill Street Blues."
- 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: Richard Klubeck Triple: [Garden State, producer, Richard Klubeck]
Generated description
Richard Klubeck is a film producer best known for his work on the acclaimed independent movie "Garden State."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Richard Klubeck Target entity description: Richard Klubeck is a film producer best known for his work on the acclaimed independent movie "Garden State."
-
A.
Richard Dybeck
Richard Dybeck was a 19th-century Swedish jurist, antiquarian, and poet best known for writing the lyrics to Sweden’s de facto national anthem, "Du gamla, Du fria."
-
B.
Paul Biegler
Paul Biegler is a small-town Michigan lawyer and the central protagonist of the courtroom drama novel and film "Anatomy of a Murder."
-
C.
Richard Gottehrer
Richard Gottehrer is an American record producer, songwriter, and music executive known for co-founding Sire Records and working with numerous influential rock and pop artists.
-
D.
Fred Schuler
Fred Schuler is a cinematographer best known for his work on films such as the 1980 comedy "Stir Crazy."
-
E.
Michael Kozoll
Michael Kozoll is an American television writer and producer best known for co-creating the influential police drama series "Hill Street Blues."
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5097fe2bc81909d66ce43f3533284 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e37382a0bc81908938b3cbdf0528e0 |
completed | April 18, 2026, 12:05 p.m. |
| NEDg | Description generation | batch_69e37ab6ca788190ac41f9494ad9a47f |
completed | April 18, 2026, 12:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e37c9439fc8190a69cfb1a13da4c19 |
completed | April 18, 2026, 12:44 p.m. |
Created at: April 6, 2026, 12:24 p.m.