Triple
T31152335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Berry (The Hotel New Hampshire) |
E794109
|
entity |
| Predicate | setInWorkGenre |
P89582
|
FINISHED |
| Object | literary fiction |
—
|
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: literary fiction | Statement: [Mary Berry (The Hotel New Hampshire), setInWorkGenre, literary fiction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInWorkGenre Context triple: [Mary Berry (The Hotel New Hampshire), setInWorkGenre, literary fiction]
-
A.
belongsToWorkGenre
Indicates that a creative work is classified under or associated with a particular genre.
-
B.
workedOnGenre
Indicates that an entity (such as a person or organization) has done work related to a particular genre.
-
C.
hasGenreOfWorkItAppearsIn
Indicates that an entity is associated with the genre of the work in which it appears.
-
D.
basedOnWorkGenre
Indicates that one entity’s genre classification is derived from or determined by the genre of another work.
-
E.
genreOfWorkSetting
chosen
Indicates the genre category that characterizes the setting in which a work takes place.
- 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_69f224d41bb48190a5621cd1485e3a30 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69c234d648190a243fb2b107136a9 |
completed | May 3, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69f69665cd9c819088c388fc82fec42e |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 29, 2026, 9:06 p.m.