Triple
T13409279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Bowen (novelist) |
E320045
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
The Girls: A Shocking Story of Love, Murder and Betrayal
"The Girls: A Shocking Story of Love, Murder and Betrayal" is a crime novel by John Bowen that delves into a dark, psychologically driven tale of relationships, deception, and violent tragedy.
|
E1039875
|
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: The Girls: A Shocking Story of Love, Murder and Betrayal | Statement: [John Bowen (novelist), notableWork, The Girls: A Shocking Story of Love, Murder and Betrayal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Girls: A Shocking Story of Love, Murder and Betrayal Context triple: [John Bowen (novelist), notableWork, The Girls: A Shocking Story of Love, Murder and Betrayal]
-
A.
The Good Girls: An Ordinary Killing
The Good Girls: An Ordinary Killing is a nonfiction book that investigates the 2014 murders of two teenage girls in rural India, exploring gender violence, caste, and systemic injustice.
-
B.
The Shining Girls
The Shining Girls is a genre-blending thriller novel by Lauren Beukes about a time-traveling serial killer and the survivor who hunts him down.
-
C.
Why Women Kill
Why Women Kill is a darkly comedic anthology drama series that explores the lives of women in different decades as they confront infidelity and its sometimes deadly consequences.
-
D.
The Girl From Plainville
The Girl From Plainville is a true-crime drama miniseries that explores the real-life "texting suicide" case of Michelle Carter and Conrad Roy III, examining the legal and emotional complexities surrounding the tragedy.
-
E.
Love, Lies and Murder
Love, Lies and Murder is a 1991 American true-crime television miniseries dramatizing the real-life murder of Linda Bailey Brown and the subsequent investigation and trial.
- 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: The Girls: A Shocking Story of Love, Murder and Betrayal Triple: [John Bowen (novelist), notableWork, The Girls: A Shocking Story of Love, Murder and Betrayal]
Generated description
"The Girls: A Shocking Story of Love, Murder and Betrayal" is a crime novel by John Bowen that delves into a dark, psychologically driven tale of relationships, deception, and violent tragedy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: The Girls: A Shocking Story of Love, Murder and Betrayal Target entity description: "The Girls: A Shocking Story of Love, Murder and Betrayal" is a crime novel by John Bowen that delves into a dark, psychologically driven tale of relationships, deception, and violent tragedy.
-
A.
The Good Girls: An Ordinary Killing
The Good Girls: An Ordinary Killing is a nonfiction book that investigates the 2014 murders of two teenage girls in rural India, exploring gender violence, caste, and systemic injustice.
-
B.
The Shining Girls
The Shining Girls is a genre-blending thriller novel by Lauren Beukes about a time-traveling serial killer and the survivor who hunts him down.
-
C.
Why Women Kill
Why Women Kill is a darkly comedic anthology drama series that explores the lives of women in different decades as they confront infidelity and its sometimes deadly consequences.
-
D.
The Girl From Plainville
The Girl From Plainville is a true-crime drama miniseries that explores the real-life "texting suicide" case of Michelle Carter and Conrad Roy III, examining the legal and emotional complexities surrounding the tragedy.
-
E.
Love, Lies and Murder
Love, Lies and Murder is a 1991 American true-crime television miniseries dramatizing the real-life murder of Linda Bailey Brown and the subsequent investigation and trial.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae4d2c5481908facfaaa1501e344 |
completed | April 12, 2026, 2:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7307ccff08190aa4037aa5a48f7d0 |
completed | May 3, 2026, 11:24 a.m. |
| NEDg | Description generation | batch_69f7313c6bcc8190a848cf8945a0ae2a |
completed | May 3, 2026, 11:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f736f2f6a081908d532dba6f34ed97 |
completed | May 3, 2026, 11:52 a.m. |
Created at: April 9, 2026, 9:35 p.m.