Triple
T21756550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Resorts World New York City |
E537055
|
entity |
| Predicate | numberOfGamingMachines |
P65801
|
FINISHED |
| Object | over 6000 |
—
|
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 6000 | Statement: [Resorts World New York City, numberOfGamingMachines, over 6000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGamingMachines Context triple: [Resorts World New York City, numberOfGamingMachines, over 6000]
-
A.
numberOfSlotMachines
chosen
Indicates the quantity of slot machines associated with a given entity or location.
-
B.
hasSlotMachines
Indicates that an entity contains, offers, or is equipped with one or more slot machines.
-
C.
numberOfReels
Indicates the quantity of reels associated with or used by an entity.
-
D.
numberOfTableGames
Indicates the quantity of table games associated with or available in relation to a given entity.
-
E.
hasGamingTables
Indicates that an entity provides or contains one or more tables specifically designated for gaming or gambling activities.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8ea04c8190a8c4fa43b3f23935 |
completed | April 28, 2026, 2:38 a.m. |
| PD | Predicate disambiguation | batch_69e6969e46088190b13d6e9025775ea3 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:50 p.m.