Triple
T33747343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The STRAT Hotel, Casino & Tower |
E864733
|
entity |
| Predicate | hasNumberOfCasinoSlots |
P65801
|
FINISHED |
| Object | over 750 |
—
|
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: over 750 | Statement: [The STRAT Hotel, Casino & Tower, hasNumberOfCasinoSlots, over 750]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCasinoSlots Context triple: [The STRAT Hotel, Casino & Tower, hasNumberOfCasinoSlots, over 750]
-
A.
numberOfSlotMachines
chosen
Indicates the quantity of slot machines associated with a given entity or location.
-
B.
hasSlotMachines
Indicates that an entity contains, offers, or is equipped with one or more slot machines.
-
C.
hasCasino
Indicates that an entity includes, contains, or is associated with a casino facility or gambling establishment.
-
D.
numberOfReels
Indicates the quantity of reels associated with or used by an entity.
-
E.
hasCasinoWebsite
Indicates that an entity operates, is associated with, or is represented by a website specifically dedicated to casino-related activities or services.
- 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_69f3498c35f881909df279ae4270f831 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe7bfc94bc81909eeec946e8c1c450 |
completed | May 9, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69fe7b74a1188190886f128e07f712da |
completed | May 9, 2026, 12:10 a.m. |
Created at: May 1, 2026, 1:45 a.m.