Triple
T32067473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Floss |
E818919
|
entity |
| Predicate | worksInFictionalIndustry |
P61294
|
FINISHED |
| Object | Hollywood entertainment industry |
—
|
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: Hollywood entertainment industry | Statement: [Al Floss, worksInFictionalIndustry, Hollywood entertainment industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worksInFictionalIndustry Context triple: [Al Floss, worksInFictionalIndustry, Hollywood entertainment industry]
-
A.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
B.
worksWithInFiction
Indicates that two fictional characters are depicted as collaborating, interacting, or being associated with each other within a narrative work.
-
C.
fictionalIndustry
chosen
Indicates that an entity operates within an industry or sector that exists only in fiction rather than in the real world.
-
D.
worksForFictionalOrganization
Indicates that an entity is employed by or affiliated as a worker with a fictional organization.
-
E.
employerFictionalIndustry
Indicates that one entity is the employer of another within a fictional or imaginary industry or professional field.
- 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_69f348fecc088190af1470afe5a969f0 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7817daf00819098936402e75ab0a6 |
completed | May 3, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 1, 2026, 12:22 a.m.