Triple
T25206715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virgin Blue |
E631271
|
entity |
| Predicate | laterLoyaltyProgramName |
P37953
|
FINISHED |
| Object | Velocity Frequent Flyer |
—
|
NE NERFINISHED |
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: Velocity Frequent Flyer | Statement: [Virgin Blue, laterLoyaltyProgramName, Velocity Frequent Flyer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterLoyaltyProgramName Context triple: [Virgin Blue, laterLoyaltyProgramName, Velocity Frequent Flyer]
-
A.
loyaltyProgramName
chosen
Indicates that an entity is associated with or identified by the name of a specific loyalty or rewards program.
-
B.
laterLoyalty
Indicates that one entity becomes loyal to another at a later time, rather than from the outset of their relationship.
-
C.
loyaltyPointName
Indicates the designated name or label assigned to a specific type or category of loyalty points in a loyalty program.
-
D.
loyaltyProgramType
Indicates the specific category or kind of loyalty program associated with an entity (such as points-based, tiered, or subscription-based).
-
E.
loyaltyMissionName
Indicates the specific mission or task associated with demonstrating or earning a character’s loyalty.
- 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_69e75a8b86c4819089eda22c843b739f |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 21, 2026, 12:52 p.m.