Triple
T5255544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caesars Palace |
E118686
|
entity |
| Predicate | hasCasinoType |
P49260
|
FINISHED |
| Object | land-based casino |
—
|
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: land-based casino | Statement: [Caesars Palace, hasCasinoType, land-based casino]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCasinoType Context triple: [Caesars Palace, hasCasinoType, land-based casino]
-
A.
hasCasino
Indicates that an entity includes, contains, or is associated with a casino facility or gambling establishment.
-
B.
hasGameType
Indicates that an entity (such as a game or match) is associated with a specific category or type of game.
-
C.
typeOfGambling
chosen
Indicates the specific category or form of gambling activity associated with an entity.
-
D.
hasHotelType
Indicates that a hotel is classified as belonging to a specific type or category (e.g., resort, boutique, hostel).
-
E.
includesGameType
Indicates that one entity contains or supports a particular type or category 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_69bd446978108190bb5f9c5c23d93f88 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7ba2f5d08190850529659901ae0f |
completed | March 20, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69bd77c30bac8190a883ca45da35d667 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:50 p.m.