Triple
T5781696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rivers Casino Des Plaines |
E127572
|
entity |
| Predicate | numberOfSlotMachines |
P65801
|
FINISHED |
| Object | over 1000 |
—
|
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 1000 | Statement: [Rivers Casino Des Plaines, numberOfSlotMachines, over 1000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSlotMachines Context triple: [Rivers Casino Des Plaines, numberOfSlotMachines, over 1000]
-
A.
numberOfReels
Indicates the quantity of reels associated with or used by an entity.
-
B.
numberOfChips
Indicates the quantity of chips associated with a given entity or situation.
-
C.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
D.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
E.
numberOfATMs
Indicates the quantitative relationship specifying how many ATMs are associated with a given entity or location.
- 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_69c008361fa88190aefa4dc41b051e7f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a17315881908aa12a830ba5f22b |
completed | March 22, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69c021d2cd608190b98a7e3aa7001d27 |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c024861bc88190a17782c1982fbb3e |
completed | March 22, 2026, 5:19 p.m. |
Created at: March 22, 2026, 3:50 p.m.