Triple
T21659709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | An American Werewolf in Paris |
E534562
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Tim Burns
Tim Burns is a screenwriter best known for co-writing the 1997 horror-comedy film "An American Werewolf in Paris."
|
E1495521
|
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: Tim Burns | Statement: [An American Werewolf in Paris, screenwriter, Tim Burns]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tim Burns Context triple: [An American Werewolf in Paris, screenwriter, Tim Burns]
-
A.
Tim Burns
Tim Burns is an actor best known for his role in the original 1979 Australian action film "Mad Max."
-
B.
Michael Burns
Michael Burns is a film producer known for his work on projects such as the psychological horror movie "Bug" (2006).
-
C.
Michael Burns
Michael Burns is an American former child actor best known for his role as Barnaby West on the classic Western television series "Wagon Train."
-
D.
Mike Burrows
Mike Burrows is a computer scientist best known for his influential work at Google on large-scale distributed systems, including co-authoring the Bigtable storage system.
-
E.
Kevin Shirley
Kevin Shirley is a renowned South African-born record producer and engineer best known for his work with major rock and metal acts such as Iron Maiden, Led Zeppelin, and Journey.
- 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: Tim Burns Triple: [An American Werewolf in Paris, screenwriter, Tim Burns]
Generated description
Tim Burns is a screenwriter best known for co-writing the 1997 horror-comedy film "An American Werewolf in Paris."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tim Burns Target entity description: Tim Burns is a screenwriter best known for co-writing the 1997 horror-comedy film "An American Werewolf in Paris."
-
A.
Tim Burns
Tim Burns is an actor best known for his role in the original 1979 Australian action film "Mad Max."
-
B.
Michael Burns
Michael Burns is a film producer known for his work on projects such as the psychological horror movie "Bug" (2006).
-
C.
Michael Burns
Michael Burns is an American former child actor best known for his role as Barnaby West on the classic Western television series "Wagon Train."
-
D.
Mike Burrows
Mike Burrows is a computer scientist best known for his influential work at Google on large-scale distributed systems, including co-authoring the Bigtable storage system.
-
E.
Kevin Shirley
Kevin Shirley is a renowned South African-born record producer and engineer best known for his work with major rock and metal acts such as Iron Maiden, Led Zeppelin, and Journey.
- 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_69e0c467e1f48190af2650b19175abc4 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c06844c81909b9c91e02fa4e6e1 |
completed | April 27, 2026, 2 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a159c33788190bf71df313526b663 |
completed | May 17, 2026, 7:23 p.m. |
| NEDg | Description generation | batch_6a0a16c278188190855335930666cede |
completed | May 17, 2026, 7:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a17a2c81481908e229ea58e61843e |
completed | May 17, 2026, 7:31 p.m. |
Created at: April 16, 2026, 6:36 p.m.