Triple
T11786058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood Casino at Charles Town Races |
E280271
|
entity |
| Predicate | hasCasinoOperations |
P24207
|
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: [Hollywood Casino at Charles Town Races, hasCasinoOperations, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCasinoOperations Context triple: [Hollywood Casino at Charles Town Races, hasCasinoOperations, yes]
-
A.
hasCasino
chosen
Indicates that an entity includes, contains, or is associated with a casino facility or gambling establishment.
-
B.
hasSlotMachines
Indicates that an entity contains, offers, or is equipped with one or more slot machines.
-
C.
majorCasinoOperatorPresence
Indicates that a major casino operator has an established operational or business presence in relation to the referenced entity or location.
-
D.
hasNearbyCasino
Indicates that one entity is located close to, or in the immediate vicinity of, a casino.
-
E.
associatedWithCasino
Indicates a relationship where an entity has a connection or involvement with a casino, such as through ownership, operation, affiliation, or regular activity.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a586803481909af0032c35ca6e51 |
completed | April 10, 2026, 7:23 a.m. |
| PD | Predicate disambiguation | batch_69d8a2491f048190853239bc05090bf4 |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.