Triple
T8085841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1st to Die |
E188728
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Cindy Thomas |
E736429
|
NE FINISHED |
How this triple was built (2 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: [1st to Die, hasCharacter, Cindy Thomas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cindy Thomas Context triple: [1st to Die, hasCharacter, Cindy Thomas]
-
A.
Cindy Thomas
chosen
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.
-
B.
Cindy Morgan
Cindy Morgan is an American actress best known for her roles in the comedy film "Caddyshack" and the science fiction film "Tron."
-
C.
Cindy Henderson
Cindy Henderson is an actress best known for voicing Wednesday Addams in the 1970s animated adaptation of The Addams Family.
-
D.
Cindy Holland
Cindy Holland is a television executive best known for her influential role in developing and overseeing original content at Netflix.
-
E.
Melissa Thomas
Melissa Thomas is known as the wife of American screenwriter and director David Koepp.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69cb4160e4748190ae63624a2a03d09f |
completed | March 31, 2026, 3:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce4d5b80b48190909ca7775fda2ed9 |
completed | April 2, 2026, 11:04 a.m. |
Created at: March 30, 2026, 5:29 p.m.