Triple
T20164772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maybank (former) |
E491799
|
entity |
| Predicate | sponsorCompanyType |
P2589
|
FINISHED |
| Object | financial institution |
—
|
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: financial institution | Statement: [Maybank (former), sponsorCompanyType, financial institution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorCompanyType Context triple: [Maybank (former), sponsorCompanyType, financial institution]
-
A.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
B.
sponsoringOrganizationType
chosen
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
C.
sponsorBrandType
Indicates the type or category of brand that is acting as a sponsor in the relationship.
-
D.
backedCompanyType
Indicates the type or category of company that is being financially supported or invested in by another party.
-
E.
sponsorParentCompany
Indicates that a parent company provides sponsorship or support to another entity, typically through funding or resources.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6684376408190a68890ab48fa5424 |
completed | April 20, 2026, 5:54 p.m. |
| PD | Predicate disambiguation | batch_69e55b0c11cc8190836d1eee5945f000 |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:35 p.m.