Triple
T11499024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shrek 2 |
E272615
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
David Lipman
David Lipman is a film producer best known for his work on major animated features, including the hit sequel "Shrek 2."
|
E929287
|
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: David Lipman | Statement: [Shrek 2, producer, David Lipman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Lipman Context triple: [Shrek 2, producer, David Lipman]
-
A.
David Resnik
David Resnik is an American bioethicist known for his work on research ethics, scientific integrity, and the ethical implications of environmental and public health policy.
-
B.
David Lanzenberg
David Lanzenberg is a film cinematographer known for his work on feature films such as "Paper Towns."
-
C.
Richard Lipton
Richard Lipton is an American computer scientist known for his influential work in theoretical computer science and cryptography, including contributions to complexity theory and algorithm design.
-
D.
David Weinberg
David Weinberg is a name shared by multiple notable individuals, including professionals in fields such as science, academia, and the arts.
-
E.
Dan Grossman
Dan Grossman is a computer scientist and professor known for his work in programming languages and software engineering.
- 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: David Lipman Triple: [Shrek 2, producer, David Lipman]
Generated description
David Lipman is a film producer best known for his work on major animated features, including the hit sequel "Shrek 2."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: David Lipman Target entity description: David Lipman is a film producer best known for his work on major animated features, including the hit sequel "Shrek 2."
-
A.
David Resnik
David Resnik is an American bioethicist known for his work on research ethics, scientific integrity, and the ethical implications of environmental and public health policy.
-
B.
David Lanzenberg
David Lanzenberg is a film cinematographer known for his work on feature films such as "Paper Towns."
-
C.
Richard Lipton
Richard Lipton is an American computer scientist known for his influential work in theoretical computer science and cryptography, including contributions to complexity theory and algorithm design.
-
D.
David Weinberg
David Weinberg is a name shared by multiple notable individuals, including professionals in fields such as science, academia, and the arts.
-
E.
Dan Grossman
Dan Grossman is a computer scientist and professor known for his work in programming languages and software engineering.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85de27db081909ccdb4ab0ef75bdb |
completed | April 10, 2026, 2:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e604aa9e3c8190ad86e4d05a67c8ac |
completed | April 20, 2026, 10:49 a.m. |
| NEDg | Description generation | batch_69e610a82c308190927158dbd566b0d4 |
completed | April 20, 2026, 11:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e61853a3b48190b0d132761e9be69c |
completed | April 20, 2026, 12:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.