Triple
T14172873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friday the 13th |
E351255
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Bill Freda
Bill Freda is a film editor best known for his work on the horror movie "Friday the 13th."
|
E1083495
|
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: Bill Freda | Statement: [Friday the 13th, editedBy, Bill Freda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Freda Context triple: [Friday the 13th, editedBy, Bill Freda]
-
A.
Vince Howard
Vince Howard is a talented high school quarterback and central character in the television series "Friday Night Lights."
-
B.
Vern Sneider
Vern Sneider was an American novelist best known for his humorous and satirical depictions of post–World War II American military occupation and cross-cultural encounters.
-
C.
Bob Frankston
Bob Frankston is an American software engineer best known as the co-creator of VisiCalc, the first widely used spreadsheet program for personal computers.
-
D.
Albert Kihn
Albert Kihn is a cinematographer best known for his work on George Lucas’s dystopian science fiction film "THX 1138."
-
E.
Ken Ford
Ken Ford is an American computer scientist and researcher known for his work in artificial intelligence and human-centered computing, particularly through his leadership at the Florida Institute for Human & Machine Cognition.
- 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: Bill Freda Triple: [Friday the 13th, editedBy, Bill Freda]
Generated description
Bill Freda is a film editor best known for his work on the horror movie "Friday the 13th."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bill Freda Target entity description: Bill Freda is a film editor best known for his work on the horror movie "Friday the 13th."
-
A.
Vince Howard
Vince Howard is a talented high school quarterback and central character in the television series "Friday Night Lights."
-
B.
Vern Sneider
Vern Sneider was an American novelist best known for his humorous and satirical depictions of post–World War II American military occupation and cross-cultural encounters.
-
C.
Bob Frankston
Bob Frankston is an American software engineer best known as the co-creator of VisiCalc, the first widely used spreadsheet program for personal computers.
-
D.
Albert Kihn
Albert Kihn is a cinematographer best known for his work on George Lucas’s dystopian science fiction film "THX 1138."
-
E.
Ken Ford
Ken Ford is an American computer scientist and researcher known for his work in artificial intelligence and human-centered computing, particularly through his leadership at the Florida Institute for Human & Machine Cognition.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b5dcbc8190b0cfcce5e6c6d582 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf80a9b34819081c4ebf7429e875a |
completed | May 7, 2026, 8:37 p.m. |
| NEDg | Description generation | batch_69fd03511f048190a9f1eea0e37aef31 |
completed | May 7, 2026, 9:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd0406a770819082aeec43037f1243 |
completed | May 7, 2026, 9:28 p.m. |
Created at: April 10, 2026, 1:01 a.m.