Triple
T26015386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Jurisdiction for Peace |
E647009
|
entity |
| Predicate | focusesOnCrimes |
P77441
|
FINISHED |
| Object | crimes against humanity |
—
|
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: crimes against humanity | Statement: [Special Jurisdiction for Peace, focusesOnCrimes, crimes against humanity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnCrimes Context triple: [Special Jurisdiction for Peace, focusesOnCrimes, crimes against humanity]
-
A.
pursuesCrimeType
chosen
Indicates that an entity (such as a law enforcement body or individual) actively investigates, targets, or prosecutes a specified type of crime.
-
B.
coversUpCrimeOf
Indicates that one entity conceals, protects, or hides the criminal actions or offenses committed by another entity.
-
C.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
D.
regionOfCrimes
Indicates the geographic area or jurisdiction in which the crimes occurred or are attributed to an entity.
-
E.
recognitionOfCrimes
Indicates the formal acknowledgment or identification that certain actions or events constitute crimes under a legal or normative framework.
- 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_69e77e8aa65881909ca58918f29ab2a0 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 22, 2026, 9:03 a.m.