Triple
T33210096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judge Hatlee Beech |
E850130
|
entity |
| Predicate | literaryGenreOfAppearance |
P161456
|
FINISHED |
| Object | crime 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: crime fiction | Statement: [Judge Hatlee Beech, literaryGenreOfAppearance, crime fiction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryGenreOfAppearance Context triple: [Judge Hatlee Beech, literaryGenreOfAppearance, crime fiction]
-
A.
literaryGenreOfWorkAppearedIn
Indicates the literary genre of the work in which a given entity (such as a text, character, or element) appears.
-
B.
literaryGenreOfWork
Indicates that a work belongs to or is classified under a particular literary genre.
-
C.
literaryGenreOfSourceWork
Indicates that a work belongs to, or is characterized by, a particular literary genre.
-
D.
literaryGenreAssociated
chosen
Indicates that there is an association between an entity and a particular literary genre with which it is related or classified.
-
E.
literatureType
Indicates the specific category or genre of literature that characterizes or classifies a given work or text.
- 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_69f3495fb92c819083ce65d0ddee7a76 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f791cc969c8190bf187d6031a030d5 |
completed | May 3, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f791033d288190b118029fe412b9c9 |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 1, 2026, 1:30 a.m.