Triple
T20689025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sharknado 3: Oh Hell No! |
E508498
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Laura Beth Love
Laura Beth Love is a cinematographer best known for her work on the cult disaster film "Sharknado 3: Oh Hell No!"
|
E1445815
|
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: Laura Beth Love | Statement: [Sharknado 3: Oh Hell No!, cinematographyBy, Laura Beth Love]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Beth Love Context triple: [Sharknado 3: Oh Hell No!, cinematographyBy, Laura Beth Love]
-
A.
Anna Beth Sully
Anna Beth Sully was the first wife of silent film star Douglas Fairbanks, whom she married before his rise to Hollywood fame.
-
B.
Laura Lane Welch
Laura Lane Welch is the birth name of Laura Bush, the former First Lady of the United States and wife of President George W. Bush.
-
C.
Jennifer Paige Scoggins
Jennifer Paige Scoggins, known professionally as Jennifer Paige, is an American pop singer best known for her late-1990s hit single "Crush."
-
D.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
E.
Mary Beth Lacey
Mary Beth Lacey is a dedicated, streetwise New York City police detective and working mother from the television series "Cagney & Lacey."
- 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: Laura Beth Love Triple: [Sharknado 3: Oh Hell No!, cinematographyBy, Laura Beth Love]
Generated description
Laura Beth Love is a cinematographer best known for her work on the cult disaster film "Sharknado 3: Oh Hell No!"
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laura Beth Love Target entity description: Laura Beth Love is a cinematographer best known for her work on the cult disaster film "Sharknado 3: Oh Hell No!"
-
A.
Anna Beth Sully
Anna Beth Sully was the first wife of silent film star Douglas Fairbanks, whom she married before his rise to Hollywood fame.
-
B.
Laura Lane Welch
Laura Lane Welch is the birth name of Laura Bush, the former First Lady of the United States and wife of President George W. Bush.
-
C.
Jennifer Paige Scoggins
Jennifer Paige Scoggins, known professionally as Jennifer Paige, is an American pop singer best known for her late-1990s hit single "Crush."
-
D.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
E.
Mary Beth Lacey
Mary Beth Lacey is a dedicated, streetwise New York City police detective and working mother from the television series "Cagney & Lacey."
- 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c10b7b808190bdb8b08e53168fb8 |
completed | April 21, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08d7e226808190bf05b85afb173e36 |
completed | May 16, 2026, 8:47 p.m. |
| NEDg | Description generation | batch_6a08d9ca43288190a2be3d4f63141391 |
completed | May 16, 2026, 8:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08da457ee48190ba18b3e34f117e19 |
completed | May 16, 2026, 8:57 p.m. |
Created at: April 16, 2026, 11:56 a.m.