Triple
T21560427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Diana Phipps |
E532003
|
entity |
| Predicate | literaryGenreOfWorkAppearedIn |
P144245
|
FINISHED |
| Object | humour |
—
|
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: humour | Statement: [Lady Diana Phipps, literaryGenreOfWorkAppearedIn, humour]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryGenreOfWorkAppearedIn Context triple: [Lady Diana Phipps, literaryGenreOfWorkAppearedIn, humour]
-
A.
literaryGenreOfWork
Indicates that a work belongs to or is classified under a particular literary genre.
-
B.
literaryGenreOfSourceWork
Indicates that a work belongs to, or is characterized by, a particular literary genre.
-
C.
isCreativeWorkOfGenre
Indicates that a creative work belongs to, or is categorized under, a particular genre.
-
D.
literatureType
Indicates the specific category or genre of literature that characterizes or classifies a given work or text.
-
E.
hasGenreInFiction
Indicates that a work of fiction belongs to or is categorized under a specific literary genre.
- 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_69e0c460232c81908de2c3819d17c00e |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eed2e2db2c81908b965312c50d4354 |
completed | April 27, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69e6320c8c2c81908bf031447d66a052 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e633bf34c481909925d8dc1a633a65 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 16, 2026, 6:29 p.m.