Triple
T26920149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Marks |
E677621
|
entity |
| Predicate | isFramedFor |
P62692
|
FINISHED |
| Object | murder |
—
|
LITERAL 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: murder | Statement: [Bill Marks, isFramedFor, murder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFramedFor Context triple: [Bill Marks, isFramedFor, murder]
-
A.
framed
chosen
Indicates that one entity has been falsely presented or set up to appear responsible or guilty for an action or situation, typically to mislead others.
-
B.
framedBy
Indicates that one entity serves as a surrounding boundary or enclosing structure that visually or conceptually frames another entity.
-
C.
usesFrame
Indicates that one entity employs, relies on, or is structured around a particular frame, framework, or reference structure provided by another entity.
-
D.
hasFramingDevice
Indicates that one entity serves as a narrative or structural framing device that contextualizes, introduces, or encloses the main content of another entity.
-
E.
hasFictionalFrame
Indicates that one entity is presented or interpreted within the context of a fictional narrative, scenario, or imaginative framework provided by another entity.
- F. None of above.
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_69eee9bdebc48190ba90a12a63e09c73 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6200cee788190ba32d1379b50ba57 |
completed | May 2, 2026, 4:02 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 6:06 a.m.