Triple
T10468613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bride and Prejudice |
E246867
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Justin Krish
Justin Krish is a film editor known for his work on the romantic drama "Bride and Prejudice."
|
E865443
|
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: Justin Krish | Statement: [Bride and Prejudice, editedBy, Justin Krish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Justin Krish Context triple: [Bride and Prejudice, editedBy, Justin Krish]
-
A.
Jared Vennett
Jared Vennett is a slick, opportunistic Wall Street trader in "The Big Short" who profits by betting against the U.S. housing market before its 2008 collapse.
-
B.
Justin Malen
Justin Malen is an American screenwriter known for writing mainstream studio comedies such as "Yes Day" and "Office Christmas Party."
-
C.
Jason Miyares
Jason Miyares is an American attorney and Republican politician who serves as the Attorney General of Virginia.
-
D.
Justin Kirk
Justin Kirk is an American actor best known for his role as Andy Botwin on the television series "Weeds" and for his work in both film and stage productions.
-
E.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
- 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: Justin Krish Triple: [Bride and Prejudice, editedBy, Justin Krish]
Generated description
Justin Krish is a film editor known for his work on the romantic drama "Bride and Prejudice."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Justin Krish Target entity description: Justin Krish is a film editor known for his work on the romantic drama "Bride and Prejudice."
-
A.
Jared Vennett
Jared Vennett is a slick, opportunistic Wall Street trader in "The Big Short" who profits by betting against the U.S. housing market before its 2008 collapse.
-
B.
Justin Malen
Justin Malen is an American screenwriter known for writing mainstream studio comedies such as "Yes Day" and "Office Christmas Party."
-
C.
Jason Miyares
Jason Miyares is an American attorney and Republican politician who serves as the Attorney General of Virginia.
-
D.
Justin Kirk
Justin Kirk is an American actor best known for his role as Andy Botwin on the television series "Weeds" and for his work in both film and stage productions.
-
E.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092ef810819093a4d1df83aeac09 |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89ff1cd948190a1ef331fb810bf26 |
completed | April 10, 2026, 7 a.m. |
| NEDg | Description generation | batch_69d8a2b0d8c88190a1a64bd2bbacabbe |
completed | April 10, 2026, 7:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8a6560ddc81909d540f78a9413b3e |
completed | April 10, 2026, 7:27 a.m. |
Created at: April 6, 2026, 12:20 p.m.