Triple
T1325359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr Mulliner stories |
E28313
|
entity |
| Predicate | frameDevice |
P26566
|
FINISHED |
| Object | Mr Mulliner recounts family anecdotes |
—
|
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: Mr Mulliner recounts family anecdotes | Statement: [Mr Mulliner stories, frameDevice, Mr Mulliner recounts family anecdotes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frameDevice Context triple: [Mr Mulliner stories, frameDevice, Mr Mulliner recounts family anecdotes]
-
A.
frameType
Indicates the specific structural or categorical kind of frame associated with an entity or relation.
-
B.
resolutionDevice
Indicates a device or instrument that is used to resolve, determine, or measure the outcome or value associated with another entity or process.
-
C.
frameworkPresented
Indicates that a particular framework has been formally introduced or shown to an audience or recipient.
-
D.
firstDevices
Indicates that the related entities are devices that occur or are used first in a sequence, order, or process relative to other devices.
-
E.
requiresDevice
Indicates that performing the specified action or relationship is contingent on the presence or use of a particular device.
- F. None of above. chosen
Provenance (4 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_69a498540a2481909e807a762280d3ba |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c19fd2648190932a85eacb3e7ec4 |
completed | March 1, 2026, 10:45 p.m. |
| PD | Predicate disambiguation | batch_69a4beedb49c8190beb5b85cdda05013 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf8158ac8190b8360ecccc2980bc |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:55 p.m.