Triple
T1521222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cipinang Prison |
E32231
|
entity |
| Predicate | countryPrisonSystem |
P30102
|
FINISHED |
| Object | Indonesian correctional system |
—
|
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: Indonesian correctional system | Statement: [Cipinang Prison, countryPrisonSystem, Indonesian correctional system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryPrisonSystem Context triple: [Cipinang Prison, countryPrisonSystem, Indonesian correctional system]
-
A.
prisonType
Indicates the specific category or classification of a prison associated with an entity.
-
B.
countryOfConviction
Indicates the country in which a person or entity was legally convicted of an offense.
-
C.
countryJurisdiction
Indicates that one country has legal authority, control, or governing power over a specified territory, entity, or matter.
-
D.
hasPrisonerPopulation
Indicates that an entity maintains or contains a population of prisoners, specifying the number or presence of incarcerated individuals associated with it.
-
E.
isCriminalizedIn
Indicates that a specific behavior, action, or condition is prohibited and subject to legal penalties within a particular jurisdiction or legal system.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93d4756888190bf3872154de11539 |
completed | March 5, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69a907ac7ea081908dd95bb5cc3b9847 |
completed | March 5, 2026, 4:33 a.m. |
| PDg | Predicate description generation | batch_69a93d462f208190b27ef5cd631bce12 |
completed | March 5, 2026, 8:22 a.m. |
Created at: March 4, 2026, 7:26 p.m.