Triple
T3017387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Safar Flyer |
E82369
|
entity |
| Predicate | accrualMethod |
P45051
|
FINISHED |
| Object | flying on Royal Air Maroc |
—
|
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: flying on Royal Air Maroc | Statement: [Safar Flyer, accrualMethod, flying on Royal Air Maroc]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accrualMethod Context triple: [Safar Flyer, accrualMethod, flying on Royal Air Maroc]
-
A.
taxationMethod
Indicates the specific way or system by which taxes are calculated, collected, or applied in a given context.
-
B.
filingMethod
Indicates how a document, record, or information is submitted or recorded, such as the process, channel, or format used for filing.
-
C.
billingMethod
Indicates the way in which payment is arranged, processed, or charged for a product, service, or account.
-
D.
calculationType
Indicates the specific method, formula, or approach used to perform a calculation in the described relationship.
-
E.
retentionMethod
Indicates the method or strategy used to retain or keep something (such as data, customers, or resources) over time.
- 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_69ad8b1eb53481908c39bbcd1ec104b2 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a90ea64819080620e60bbd6aa24 |
completed | March 8, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69ad961a97188190809dc73430a8eda8 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97ba55dc8190b6dddddfb751cf64 |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 8, 2026, 3 p.m.