Triple
T34589010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marjorie Liu |
E888123
|
entity |
| Predicate | hasWrittenShortFiction |
P78982
|
FINISHED |
| Object | Hunter Kiss stories |
—
|
NE NERFINISHED |
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: Hunter Kiss stories | Statement: [Marjorie Liu, hasWrittenShortFiction, Hunter Kiss stories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWrittenShortFiction Context triple: [Marjorie Liu, hasWrittenShortFiction, Hunter Kiss stories]
-
A.
hasWrittenFiction
chosen
Indicates that one entity is the creator or author of fictional written works associated with another entity.
-
B.
hasWrittenNonFiction
Indicates that a person is the author of one or more non-fiction works.
-
C.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
D.
hasAssociatedWorkOfFiction
Indicates that an entity is linked to a related work of fiction, such as a novel, film, or story that is associated with it.
-
E.
hasWrittenAbout
Indicates that one entity has authored content or material discussing, analyzing, or referencing another entity.
- 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_69f349d3bfcc81909874c99e646fb3ea |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7234bcaa48190ac970759d34e254a |
completed | May 3, 2026, 10:28 a.m. |
| PD | Predicate disambiguation | batch_69f72155c48881909bd40b9aa3febd5a |
completed | May 3, 2026, 10:20 a.m. |
Created at: May 1, 2026, 2:03 a.m.