Triple
T21307766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Resorts Casino Hotel |
E525243
|
entity |
| Predicate | hasTypeOfGambling |
P49260
|
FINISHED |
| Object | slot machines |
—
|
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: slot machines | Statement: [Resorts Casino Hotel, hasTypeOfGambling, slot machines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfGambling Context triple: [Resorts Casino Hotel, hasTypeOfGambling, slot machines]
-
A.
typeOfGambling
chosen
Indicates the specific category or form of gambling activity associated with an entity.
-
B.
hasGamblingHabitLocation
Indicates the place or setting where an entity’s gambling habit is regularly carried out or expressed.
-
C.
typeOfGamblingVenue
Indicates that one entity is a specific kind or category of gambling venue in relation to another entity.
-
D.
hasCasino
Indicates that an entity includes, contains, or is associated with a casino facility or gambling establishment.
-
E.
hasGameType
Indicates that an entity (such as a game or match) is associated with a specific category or type of game.
- 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_69e0b518b8948190ad69cf9a8784d397 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75aa7b2f08190bea46f0107bcc045 |
completed | April 21, 2026, 11:08 a.m. |
| PD | Predicate disambiguation | batch_69e61612ab748190a72b8703b938abcb |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 4:05 p.m.