Triple
T12236732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Resorts World Las Vegas |
E291613
|
entity |
| Predicate | numberOfRestaurantsAndBars |
P57594
|
FINISHED |
| Object | 40+ |
—
|
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: 40+ | Statement: [Resorts World Las Vegas, numberOfRestaurantsAndBars, 40+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRestaurantsAndBars Context triple: [Resorts World Las Vegas, numberOfRestaurantsAndBars, 40+]
-
A.
hasNumberOfRestaurantsAndBars
chosen
Indicates the total count of restaurants and bars associated with a given entity.
-
B.
numberOfRestaurantsAndCafes
Indicates the total count of restaurants and cafes associated with a given entity or area.
-
C.
hasRestaurantsAndBars
Indicates that the subject location contains or provides access to both restaurants and bars.
-
D.
numberOfRestaurants
Indicates the quantitative count of restaurants associated with a given entity or context.
-
E.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d924a3973c8190a882046963b320fb |
completed | April 10, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69d91c41bcbc81909782f4e3c571b218 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.