Triple
T25660706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bijstandseenheid Marechaussee |
E643367
|
entity |
| Predicate | isLawEnforcement |
P158964
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Bijstandseenheid Marechaussee, isLawEnforcement, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLawEnforcement Context triple: [Bijstandseenheid Marechaussee, isLawEnforcement, true]
-
A.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
-
B.
lawEnforcementLabel
Indicates that an entity has been designated, tagged, or classified by a law enforcement authority for monitoring, identification, or investigative purposes.
-
C.
lawEnforcementStatus
Indicates the relationship between an entity and its current standing or condition with respect to law enforcement, such as being under investigation, wanted, detained, or cleared.
-
D.
isMilitaryOfficer
Indicates that the subject holds an official position as an officer within a military organization.
-
E.
associatedWithLawEnforcementAgency
Indicates that an entity has a formal connection, role, or involvement with a law enforcement agency.
- 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_69e77e7e45648190a068ed3faa8016ea |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5faef72a88190b6401a319638ff14 |
completed | May 2, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69f4807f8680819098a524158d049c63 |
completed | May 1, 2026, 10:29 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 21, 2026, 6:52 p.m.