Triple
T36849361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pakistan Special Police Establishment |
E910633
|
entity |
| Predicate | handledCrimes |
P186581
|
FINISHED |
| Object | corruption by public officials |
—
|
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: corruption by public officials | Statement: [Pakistan Special Police Establishment, handledCrimes, corruption by public officials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: handledCrimes Context triple: [Pakistan Special Police Establishment, handledCrimes, corruption by public officials]
-
A.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
B.
crimeCharged
Indicates that legal authorities have formally accused an entity of committing a specific crime.
-
C.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
D.
perpetratedCrimesIn
Indicates that an entity committed one or more crimes within a specified location or jurisdiction.
-
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. 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_69f76e8033d48190a59274f86f13be48 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fe1a1ca4819084c196f0041f0be2 |
completed | May 5, 2026, 2:26 p.m. |
| PD | Predicate disambiguation | batch_69f7cf7890008190a8bc355ff2d61c86 |
completed | May 3, 2026, 10:43 p.m. |
| PDg | Predicate description generation | batch_69f9fd66eed48190bdc26a8def328c2d |
completed | May 5, 2026, 2:23 p.m. |
Created at: May 3, 2026, 4:13 p.m.