Triple
T22413569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derek Klena |
E554053
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Carrie |
—
|
NE NERFINISHED |
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: Carrie | Statement: [Derek Klena, notableWork, Carrie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carrie Context triple: [Derek Klena, notableWork, Carrie]
-
A.
Carrie
chosen
Carrie is the charming and enigmatic American woman who becomes the central love interest in the British romantic comedy film "Four Weddings and a Funeral."
-
B.
Carrie
Carrie is a feminine given name commonly used in English-speaking countries, often as a diminutive of Caroline or Carol.
-
C.
Carrie
"Carrie" is Stephen King's debut horror novel, centered on a bullied teenage girl with telekinetic powers who exacts a devastating revenge on her tormentors.
-
D.
Misery
Misery is the first major section of the Heidelberg Catechism, focusing on humanity’s sinfulness and need for redemption.
-
E.
Misery
Misery is a 1990 psychological horror film, based on Stephen King’s novel, about a famous author held captive by an obsessive fan after a car accident.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1594496348190ba25dc0193d092f2 |
completed | April 29, 2026, 1:05 a.m. |
Created at: April 16, 2026, 8:46 p.m.