Triple
T11466404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regal |
E271791
|
entity |
| Predicate | loyaltyProgram |
P178
|
FINISHED |
| Object | Regal Unlimited |
E271790
|
NE 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: Regal Unlimited | Statement: [Regal, loyaltyProgram, Regal Unlimited]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Regal Unlimited Context triple: [Regal, loyaltyProgram, Regal Unlimited]
-
A.
Regal Unlimited
chosen
Regal Unlimited is a movie theater subscription service that lets members watch multiple films at Regal Cinemas for a flat monthly fee.
-
B.
Nautica
Nautica is an American lifestyle brand best known for its nautical-inspired apparel and accessories.
-
C.
Ocean Star
Ocean Star is a retired offshore drilling rig converted into a museum in Galveston, Texas, that educates visitors about the offshore oil and gas industry.
-
D.
Regal GS
The Regal GS is a performance-oriented variant of the Buick Regal, featuring a sport-tuned chassis, more powerful engine options, and upgraded styling and interior appointments.
-
E.
Lisberg
Lisberg is a Danish-origin surname most notably associated with figures such as Jens Oliver Lisberg.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f5eb988190b309b8e309f6d1a5 |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e9429a308190810b485708d28617 |
completed | April 20, 2026, 8:52 a.m. |
Created at: April 8, 2026, 9:35 p.m.