Triple
T21585223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roxy’s |
E532632
|
entity |
| Predicate | hasTitle |
P38
|
FINISHED |
| Object | Roxy’s |
—
|
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: Roxy’s | Statement: [Roxy’s, hasTitle, Roxy’s]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roxy’s Context triple: [Roxy’s, hasTitle, Roxy’s]
-
A.
Roxy’s
chosen
Roxy’s is a major installation artwork by American assemblage artist Ed Kienholz that recreates a 1940s brothel in a gritty, immersive, and socially critical environment.
-
B.
Rosie’s
Rosie’s is the women’s jail facility on New York City’s Rikers Island, officially known as the Rose M. Singer Center.
-
C.
Sunnyside Café
Sunnyside Café is a themed dining venue located within the Toy Story Hotel, offering guests a playful, Pixar-inspired restaurant experience.
-
D.
Ronto Roasters
Ronto Roasters is a quick-service food stand in the Star Wars: Galaxy’s Edge themed land, known for its grilled wraps and immersive, market-style theming.
-
E.
Hungry Corner
Hungry Corner is a notably challenging turn at Lakeside Raceway, known among drivers for its demanding line and impact on lap times.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb60032881908dd35ac76392ad07 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 16, 2026, 6:31 p.m.