Triple
T6894373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GarudaMiles |
E159132
|
entity |
| Predicate | primaryBenefitCategory |
P25020
|
FINISHED |
| Object | travel benefits |
—
|
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: travel benefits | Statement: [GarudaMiles, primaryBenefitCategory, travel benefits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryBenefitCategory Context triple: [GarudaMiles, primaryBenefitCategory, travel benefits]
-
A.
primaryBenefit
chosen
Indicates that one entity serves as the main or most important advantage, gain, or positive outcome associated with another entity.
-
B.
primaryProduct
Indicates that one entity is the main or most important product associated with, produced by, or offered by another entity.
-
C.
primaryBeneficiaries
Indicates which entities are the main recipients or advantaged parties resulting from a particular action, resource, or arrangement.
-
D.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
E.
primaryType
Indicates the main or most fundamental category or classification assigned to an entity, distinguishing it from any secondary or auxiliary types.
- 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.