Triple
T1182314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aventura Hotel at Universal Orlando |
E25164
|
entity |
| Predicate | guestBenefit |
P2188
|
FINISHED |
| Object | Early Park Admission to select Universal parks |
—
|
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: Early Park Admission to select Universal parks | Statement: [Aventura Hotel at Universal Orlando, guestBenefit, Early Park Admission to select Universal parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: guestBenefit Context triple: [Aventura Hotel at Universal Orlando, guestBenefit, Early Park Admission to select Universal parks]
-
A.
hasBenefit
chosen
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
B.
fareDiscount
Indicates that a reduced price is applied to a standard fare for a product or service.
-
C.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
D.
hasHospitalityComponent
Indicates that something includes, involves, or is associated with a hospitality-related element, service, or function.
-
E.
supportsBonus
Indicates that one entity provides or enables an additional benefit, reward, or bonus for 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_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd347d4481909e9094463011289d |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb59ca6c81908597a81646674aaa |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.