Triple
T8800091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scarlet Lady |
E209381
|
entity |
| Predicate | hasBeveragePolicy |
P84697
|
FINISHED |
| Object | basic beverages included in fare |
—
|
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: basic beverages included in fare | Statement: [Scarlet Lady, hasBeveragePolicy, basic beverages included in fare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeveragePolicy Context triple: [Scarlet Lady, hasBeveragePolicy, basic beverages included in fare]
-
A.
hasBeverageProgram
Indicates that an entity offers, manages, or participates in an organized beverage-related offering or initiative.
-
B.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
C.
hasBeverageReference
Indicates that one entity makes reference to, mentions, or is associated with a beverage in some way.
-
D.
hasDiningPolicy
Indicates that an entity enforces or follows a specific set of rules or guidelines related to dining or meal-related activities.
-
E.
drinkingPermitted
Indicates that consuming alcoholic beverages is allowed in a given context, location, or situation.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb8aab88190befed16301e08efc |
completed | March 31, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cff3608819081d2d7e5c16d44b7 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:44 p.m.