Triple
T10190296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Casino Rama Resort |
E238014
|
entity |
| Predicate | hasSlotMachines |
P92611
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Casino Rama Resort, hasSlotMachines, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSlotMachines Context triple: [Casino Rama Resort, hasSlotMachines, yes]
-
A.
numberOfSlotMachines
Indicates the quantity of slot machines associated with a given entity or location.
-
B.
hasCasino
Indicates that an entity includes, contains, or is associated with a casino facility or gambling establishment.
-
C.
hasArcades
Indicates that one entity features or contains arcaded structures (a series of arches or covered passageways) associated with another entity.
-
D.
majorCasinoOperatorPresence
Indicates that a major casino operator has an established operational or business presence in relation to the referenced entity or location.
-
E.
slot
Indicates a relationship where an entity is assigned or occupies a specific position, time, or opening within an ordered or limited set of available places.
- F. None of above. chosen
Provenance (4 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded7d6fdc81908052866495b6574f |
completed | April 2, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8477648190bc55c56aeec507d3 |
completed | April 1, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69cd7edc6cf081909d95859d880a4059 |
completed | April 1, 2026, 8:23 p.m. |
Created at: March 30, 2026, 9:13 p.m.