Triple
T33747344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The STRAT Hotel, Casino & Tower |
E864733
|
entity |
| Predicate | hasNumberOfTableGames |
P65802
|
FINISHED |
| Object | over 40 |
—
|
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 40 | Statement: [The STRAT Hotel, Casino & Tower, hasNumberOfTableGames, over 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfTableGames Context triple: [The STRAT Hotel, Casino & Tower, hasNumberOfTableGames, over 40]
-
A.
numberOfTableGames
chosen
Indicates the quantity of table games associated with or available in relation to a given entity.
-
B.
hasGamingTables
Indicates that an entity provides or contains one or more tables specifically designated for gaming or gambling activities.
-
C.
hasNumberOfHands
Indicates the specific count of hands that an entity possesses.
-
D.
tieGamesCount
Indicates the number of games in a set or series that ended in a tie, with no winner or loser.
-
E.
numberOfGamesFormat
Indicates the total count of distinct game formats associated with or used in a given context.
- 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_69fe7eb4b8348190bb19d35766189ed4 |
completed | May 9, 2026, 12:24 a.m. |
| PD | Predicate disambiguation | batch_69fe7c35d2148190ab952e54feda1e76 |
completed | May 9, 2026, 12:13 a.m. |
Created at: May 1, 2026, 1:45 a.m.