Triple
T31820834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | People’s Business Credit |
E812252
|
entity |
| Predicate | fullNameIndonesian |
P24145
|
FINISHED |
| Object | Kredit Usaha Rakyat |
E44909
|
NE 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: Kredit Usaha Rakyat | Statement: [People’s Business Credit, fullNameIndonesian, Kredit Usaha Rakyat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fullNameIndonesian Context triple: [People’s Business Credit, fullNameIndonesian, Kredit Usaha Rakyat]
-
A.
nameInIndonesian
chosen
Indicates that an entity is referred to by a specified name in the Indonesian language.
-
B.
hasJavaneseName
Indicates that an entity possesses a name expressed in the Javanese language.
-
C.
hasNameInBalinese
Indicates that an entity is associated with a specific name expressed in the Balinese language.
-
D.
hasMalayName
Indicates that an entity is associated with a specific name expressed in the Malay language.
-
E.
IDN
Indicates that two entities are identical in value, reference, or identity, representing exact sameness rather than mere similarity.
- F. None of above.
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_69f348e97fa48190aa06286962af6dee |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6af7f24a881909e6ae0d937e90ea2 |
completed | May 3, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2d9d751ca88190a3a0aaa1c67d6dbc |
completed | June 13, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69f6aca59d4881908d14ed47962703bd |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 30, 2026, 11:45 p.m.