Triple
T32932966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franklin W. Dixon |
E842445
|
entity |
| Predicate | hasFictionalAuthorRole |
P68311
|
FINISHED |
| Object | author of Hardy Boys |
—
|
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: author of Hardy Boys | Statement: [Franklin W. Dixon, hasFictionalAuthorRole, author of Hardy Boys]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalAuthorRole Context triple: [Franklin W. Dixon, hasFictionalAuthorRole, author of Hardy Boys]
-
A.
hasFictionalAuthor
chosen
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
B.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
D.
worksWithFictionalCharacter
Indicates that one entity collaborates or interacts in a work-related context with another entity that is a fictional character.
-
E.
hasFictionalPerformer
Indicates that an entity is associated with a performer who is a fictional or imaginary character rather than a real person.
- 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_69fefa064ab48190925759950d0d94d9 |
completed | May 9, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_69fef96ae5d08190b027435753c44821 |
completed | May 9, 2026, 9:07 a.m. |
Created at: May 1, 2026, 1:20 a.m.