Triple
T824291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trump International Hotel & Tower |
E17818
|
entity |
| Predicate | hasAmenityType |
P2836
|
FINISHED |
| Object | fitness center |
—
|
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: fitness center | Statement: [Trump International Hotel & Tower, hasAmenityType, fitness center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAmenityType Context triple: [Trump International Hotel & Tower, hasAmenityType, fitness center]
-
A.
hasFacilityType
chosen
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
B.
amenityLevel
Indicates the degree or quality of facilities, services, or conveniences provided in relation to something.
-
C.
hasAttractionType
Indicates that one entity is associated with a specific kind or category of attraction (e.g., tourist, cultural, natural).
-
D.
amenity
Indicates that one entity provides a useful facility, service, or feature that enhances the convenience or comfort of another entity.
-
E.
hasAccommodation
Indicates that an entity provides, owns, or is associated with a place for someone to stay or live.
- 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ab7d3984819089aefbf12d3b3c2c |
completed | March 1, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69a4aa781e1081909df006f730296c53 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.