Triple
T32932190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carolyn Keene |
E842426
|
entity |
| Predicate | fictionalAuthorOf |
P68311
|
FINISHED |
| Object | in-universe Nancy Drew 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: in-universe Nancy Drew books | Statement: [Carolyn Keene, fictionalAuthorOf, in-universe Nancy Drew books]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalAuthorOf Context triple: [Carolyn Keene, fictionalAuthorOf, in-universe Nancy Drew books]
-
A.
hasFictionalAuthor
chosen
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
B.
fictionalAuthorOrigin
Indicates that a fictional author character originates from, or is associated with, a particular place or region.
-
C.
fictionalUniverseAuthor
Indicates that an author is the creator or primary writer responsible for a given fictional universe.
-
D.
literaryAuthor
Indicates that one entity is the author or writer of a literary work represented by the other entity.
-
E.
favoriteAuthor
Indicates that one entity is the author whom another entity prefers above all others.
- 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_69f34948adfc8190a937f1f622783c0b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fe0d165a48819098b854318a50d76c |
completed | May 8, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69fe0931002481908a95b34f95e9f64e |
completed | May 8, 2026, 4:02 p.m. |
Created at: May 1, 2026, 1:20 a.m.