Triple
T6894356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GarudaMiles |
E159132
|
entity |
| Predicate | eligibleCustomerType |
P809
|
FINISHED |
| Object | individual travelers |
—
|
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: individual travelers | Statement: [GarudaMiles, eligibleCustomerType, individual travelers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eligibleCustomerType Context triple: [GarudaMiles, eligibleCustomerType, individual travelers]
-
A.
customerType
chosen
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
B.
eligibilityCategory
Indicates the classification or type of eligibility that applies to an entity within a given context.
-
C.
eligibleBorrower
Indicates that an entity meets the required conditions to be allowed to borrow (e.g., money, items, or resources) under a given set of rules or policies.
-
D.
eligibleMembers
Indicates that certain entities meet the required criteria or conditions to be considered eligible members of a specified group or category.
-
E.
eligibleBusinessType
Indicates that a business entity qualifies under specified criteria to be considered an eligible type for a particular program, rule, or context.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d931da24819096b9b205f2c0ebb0 |
completed | March 27, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b7681481909ec50509b19fcf81 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:24 p.m.