Triple
T3331490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEN/Faulkner Award for Fiction |
E70041
|
entity |
| Predicate | mediaTypeOfWorks |
P42896
|
FINISHED |
| Object | printed books |
—
|
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: printed books | Statement: [PEN/Faulkner Award for Fiction, mediaTypeOfWorks, printed books]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaTypeOfWorks Context triple: [PEN/Faulkner Award for Fiction, mediaTypeOfWorks, printed books]
-
A.
mediaType
Indicates the format or category of media associated with an entity, such as text, image, audio, or video.
-
B.
artworkType
Indicates the specific category or kind of artwork that characterizes the relationship between the subject and the artwork.
-
C.
mediaWork
Indicates a relationship where one entity is a media-related work (such as a film, book, recording, or other creative media production) associated with another entity.
-
D.
creativeWorkType
chosen
Indicates the specific category or form of a creative work (such as book, movie, painting, or song) that characterizes the relationship.
-
E.
artisticMedium
Indicates the material or technique used to create an artwork or artistic expression.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb191e1988190a1d88596f6605aff |
completed | March 8, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69ada42c2ba8819091136805ce17b39d |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:12 p.m.