Triple
T26417064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roulette |
E664124
|
entity |
| Predicate | hasHouseEdge |
P172771
|
FINISHED |
| Object | 2.7% in single-zero European roulette (approximate) |
—
|
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: 2.7% in single-zero European roulette (approximate) | Statement: [Roulette, hasHouseEdge, 2.7% in single-zero European roulette (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHouseEdge Context triple: [Roulette, hasHouseEdge, 2.7% in single-zero European roulette (approximate)]
-
A.
hasCasino
Indicates that an entity includes, contains, or is associated with a casino facility or gambling establishment.
-
B.
hasOutsideBet
Indicates that an entity has placed or holds a bet on an outcome considered an outside or less likely option in a given betting context.
-
C.
hasSlotMachines
Indicates that an entity contains, offers, or is equipped with one or more slot machines.
-
D.
hasGamingTables
Indicates that an entity provides or contains one or more tables specifically designated for gaming or gambling activities.
-
E.
hasBettingStructure
Indicates that there is a specific set of rules or format governing how bets are placed and progressed in a game or wagering context.
- 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_69ee883a04ec81908883c4559f8c7e24 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f6b0d21dd08190a9883ff71c94c71c |
completed | May 3, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
| PDg | Predicate description generation | batch_69f6afeaaef88190aefa97e83f8db906 |
completed | May 3, 2026, 2:16 a.m. |
Created at: April 26, 2026, 11:41 p.m.