Triple
T8085480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Women's Murder Club series |
E188720
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Cindy Thomas
Cindy Thomas is a tenacious crime reporter and one of the core members of the Women's Murder Club in James Patterson's mystery novel series.
|
E736429
|
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: Cindy Thomas | Statement: [Women's Murder Club series, hasMainCharacter, Cindy Thomas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cindy Thomas Context triple: [Women's Murder Club series, hasMainCharacter, Cindy Thomas]
-
A.
Cindy Morgan
Cindy Morgan is an American actress best known for her roles in the comedy film "Caddyshack" and the science fiction film "Tron."
-
B.
Cindy Henderson
Cindy Henderson is an actress best known for voicing Wednesday Addams in the 1970s animated adaptation of The Addams Family.
-
C.
Cindy Holland
Cindy Holland is a television executive best known for her influential role in developing and overseeing original content at Netflix.
-
D.
Melissa Thomas
Melissa Thomas is known as the wife of American screenwriter and director David Koepp.
-
E.
Cindy Pickett
Cindy Pickett is an American actress best known for her role as Ferris Bueller’s mother in the classic 1986 teen comedy film "Ferris Bueller’s Day Off."
- 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: Cindy Thomas Triple: [Women's Murder Club series, hasMainCharacter, Cindy Thomas]
Generated description
Cindy Thomas is a tenacious crime reporter and one of the core members of the Women's Murder Club in James Patterson's mystery novel series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cindy Thomas Target entity description: Cindy Thomas is a tenacious crime reporter and one of the core members of the Women's Murder Club in James Patterson's mystery novel series.
-
A.
Cindy Morgan
Cindy Morgan is an American actress best known for her roles in the comedy film "Caddyshack" and the science fiction film "Tron."
-
B.
Cindy Henderson
Cindy Henderson is an actress best known for voicing Wednesday Addams in the 1970s animated adaptation of The Addams Family.
-
C.
Cindy Holland
Cindy Holland is a television executive best known for her influential role in developing and overseeing original content at Netflix.
-
D.
Melissa Thomas
Melissa Thomas is known as the wife of American screenwriter and director David Koepp.
-
E.
Cindy Pickett
Cindy Pickett is an American actress best known for her role as Ferris Bueller’s mother in the classic 1986 teen comedy film "Ferris Bueller’s Day Off."
- 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb415f73808190b69db386b447062e |
completed | March 31, 2026, 3:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce38eaf93481908939e770f0dda7f5 |
completed | April 2, 2026, 9:37 a.m. |
| NEDg | Description generation | batch_69ce3b1e188c8190ad894478141f6501 |
completed | April 2, 2026, 9:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce3bfd00948190b3956be3f8c5d547 |
completed | April 2, 2026, 9:50 a.m. |
Created at: March 30, 2026, 5:29 p.m.