Triple
T33653341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Orchid Plus miles |
E862155
|
entity |
| Predicate | earntBasedOn |
P54922
|
FINISHED |
| Object | booking class |
—
|
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: booking class | Statement: [Royal Orchid Plus miles, earntBasedOn, booking class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earntBasedOn Context triple: [Royal Orchid Plus miles, earntBasedOn, booking class]
-
A.
earntBy
Indicates that something (such as money, points, or a reward) is obtained as a result of the actions, work, or performance of a particular entity.
-
B.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
C.
learnedIn
Indicates that an entity acquired knowledge, skills, or information within a particular context, environment, or source.
-
D.
basedOnBy
chosen
Indicates that one entity is derived from, justified by, or constructed using another entity as its source, foundation, or reference.
-
E.
usesLearningMechanism
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
- 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_69f349840ba881908e3bfce536aeb92b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6ffbad8848190867c2988c0ceb84f |
completed | May 3, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69f6fc5740fc81909774a4f65201a3ff |
completed | May 3, 2026, 7:42 a.m. |
Created at: May 1, 2026, 1:42 a.m.