Triple
T35471191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nikki Heat book series |
E1025211
|
entity |
| Predicate | creditedAuthorIsFictional |
P68311
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Nikki Heat book series, creditedAuthorIsFictional, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: creditedAuthorIsFictional Context triple: [Nikki Heat book series, creditedAuthorIsFictional, true]
-
A.
hasFictionalAuthor
chosen
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
B.
authorshipInFiction
Indicates that one entity is the creator or writer of a fictional work in which another entity appears or is represented.
-
C.
fictionalAuthorVictim
Indicates that one entity is the author of a fictional work in which the other entity appears as a victim.
-
D.
responsibleForFictional
Indicates that one entity bears responsibility for creating, managing, or causing a fictional work, character, event, or universe associated with another entity.
-
E.
actuallyWrittenBy
Indicates that the specified work was in fact authored by the given entity, possibly correcting or overriding a previously assumed or credited author.
- 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_69f76dfadba0819083456aadcd6864ea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:04 p.m.