Triple
T21524387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Proof (film) |
E531056
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Jeffrey Sharp
Jeffrey Sharp is an American film producer known for his work on acclaimed independent movies and literary adaptations.
|
E1504427
|
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: Jeffrey Sharp | Statement: [Proof (film), producer, Jeffrey Sharp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Sharp Context triple: [Proof (film), producer, Jeffrey Sharp]
-
A.
Kevin Sharp
Kevin Sharp is a name shared by several notable individuals, including an American country music singer and a British judge.
-
B.
Michael Sharp
Michael Sharp is a relatively common personal name that may refer to multiple individuals across different fields, such as academia, the arts, or public service.
-
C.
Jeffrey Heath
Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
-
D.
Jeffrey Paley
Jeffrey Paley is the son of longtime CBS chairman William S. Paley and a member of the prominent Paley media family.
-
E.
Jeffrey Winston
Jeffrey Winston is known as the former husband of American actress Debbi Morgan.
- 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: Jeffrey Sharp Triple: [Proof (film), producer, Jeffrey Sharp]
Generated description
Jeffrey Sharp is an American film producer known for his work on acclaimed independent movies and literary adaptations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Sharp Target entity description: Jeffrey Sharp is an American film producer known for his work on acclaimed independent movies and literary adaptations.
-
A.
Kevin Sharp
Kevin Sharp is a name shared by several notable individuals, including an American country music singer and a British judge.
-
B.
Michael Sharp
Michael Sharp is a relatively common personal name that may refer to multiple individuals across different fields, such as academia, the arts, or public service.
-
C.
Jeffrey Heath
Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
-
D.
Jeffrey Paley
Jeffrey Paley is the son of longtime CBS chairman William S. Paley and a member of the prominent Paley media family.
-
E.
Jeffrey Winston
Jeffrey Winston is known as the former husband of American actress Debbi Morgan.
- 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_69e0c45d95a081908e7962ad215da746 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee884f4504819086bd632e62f02f58 |
completed | April 26, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a4bb385888190b27aca9e5864cf15 |
completed | May 17, 2026, 11:13 p.m. |
| NEDg | Description generation | batch_6a0a4c9a02bc8190b285694c2710b825 |
completed | May 17, 2026, 11:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a4d5d1b448190ba4387b0a3cc4285 |
completed | May 17, 2026, 11:21 p.m. |
Created at: April 16, 2026, 6:26 p.m.