Triple
T6373185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caesars Rewards |
E143398
|
entity |
| Predicate | offersStatusBenefits |
P2188
|
FINISHED |
| Object | priority lines |
—
|
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: priority lines | Statement: [Caesars Rewards, offersStatusBenefits, priority lines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersStatusBenefits Context triple: [Caesars Rewards, offersStatusBenefits, priority lines]
-
A.
hasBenefit
chosen
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
B.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
C.
offersStatusMatchPromotions
Indicates that one entity provides status match promotional offers to another entity, typically allowing them to match or upgrade their existing status from a different program.
-
D.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
E.
offersProgram
Indicates that an entity provides or makes available a specific program (such as a course, curriculum, or initiative).
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c068390af08190967a3d25ec9a50e5 |
completed | March 22, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69c060ee055081908c79a1d151bd74cd |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:33 p.m.