Triple
T1568795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empire City Casino |
E33491
|
entity |
| Predicate | gamingLicenseType |
P19269
|
FINISHED |
| Object | video lottery terminal license |
—
|
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: video lottery terminal license | Statement: [Empire City Casino, gamingLicenseType, video lottery terminal license]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gamingLicenseType Context triple: [Empire City Casino, gamingLicenseType, video lottery terminal license]
-
A.
typeOfLicense
chosen
Indicates the specific kind or category of license associated with an entity.
-
B.
includesGameType
Indicates that one entity contains or supports a particular type or category of game.
-
C.
hasGameType
Indicates that an entity (such as a game or match) is associated with a specific category or type of game.
-
D.
notableGameType
Indicates that a game is of a particular type or category for which the subject is especially well known or notable.
-
E.
licenseFamily
Indicates that one license belongs to, is derived from, or is categorized under a broader family or class of related licenses.
- 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_69a885f11b048190935025a035302715 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90fccd4b48190a44012888a00af7f |
completed | March 5, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69a907b872f0819096b3df6ad502c63e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.