Triple
T35882028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julie Nichols |
E1037535
|
entity |
| Predicate | partOfFictionalWorkType |
P194238
|
FINISHED |
| Object | feature film |
—
|
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: feature film | Statement: [Julie Nichols, partOfFictionalWorkType, feature film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfFictionalWorkType Context triple: [Julie Nichols, partOfFictionalWorkType, feature film]
-
A.
partOfFictionalCorpus
chosen
Indicates that one fictional work, element, or text belongs to or is contained within a larger fictional collection, series, or universe.
-
B.
fictionalUniverseWorkType
Indicates the type or category of creative work (e.g., book, film, game) in which a given fictional universe appears or is expressed.
-
C.
isFictionalWorkWithinWork
Indicates that one fictional work is contained or presented as a subordinate or embedded work within another fictional work.
-
D.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
E.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with 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_69f76e1f4d748190bb55594d8441d70e |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a00ae638fcc819096537b2c21366161 |
completed | May 10, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_6a00ae0551988190bcb3564ed9af2847 |
completed | May 10, 2026, 4:10 p.m. |
Created at: May 3, 2026, 4:06 p.m.