Triple
T20997663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Strickland |
E517191
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | Flux Gourmet |
—
|
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: Flux Gourmet | Statement: [Peter Strickland, wrote, Flux Gourmet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flux Gourmet Context triple: [Peter Strickland, wrote, Flux Gourmet]
-
A.
Flux Gourmet
chosen
Flux Gourmet is a surreal black comedy film that satirizes the world of experimental performance art and food culture.
-
B.
Blodgett
Blodgett is a surname of English origin borne by various notable individuals across fields such as politics, business, and the arts.
-
C.
Flux
Flux is a hard science fiction novel by Stephen Baxter that explores life in extreme environments on a subatomic scale within a neutron star.
-
D.
Flux
Flux is a GitOps-based continuous delivery tool for Kubernetes that automates deploying and reconciling application state from version-controlled configuration.
-
E.
Flux
Flux is an interactive light-based art installation by American artist Jen Lewin, known for its immersive, technology-driven experience that responds to viewers’ movements.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc22ca6081908bf054ddcfea9e19 |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:51 p.m.