Triple
T9925771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Law Enforcement Command of the Islamic Republic of Iran |
E187916
|
entity |
| Predicate | usesVehicleMarking |
P30201
|
FINISHED |
| Object | Iranian police livery |
—
|
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: Iranian police livery | Statement: [Law Enforcement Command of the Islamic Republic of Iran, usesVehicleMarking, Iranian police livery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesVehicleMarking Context triple: [Law Enforcement Command of the Islamic Republic of Iran, usesVehicleMarking, Iranian police livery]
-
A.
colorOfTrailMarkings
Indicates the relationship specifying what color the trail’s markings are.
-
B.
usedOnOfficialPlatesIn
Indicates that something is employed or displayed on official license plates within a specified jurisdiction or region.
-
C.
usesRollingStockBrand
Indicates that one entity employs or operates rolling stock manufactured under a specific brand.
-
D.
mayHaveMarkings
chosen
Indicates that an entity is permitted or able to possess certain markings or distinguishing signs.
-
E.
usesArmoredVehicle
Indicates that an entity employs or operates an armored vehicle in performing an action or fulfilling a role.
- 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_69ca82b22a688190b52c75bd48429c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb599e32c8190ac676fa89c131bb6 |
completed | April 2, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69cd1d90b8a8819081748f129c0c6ab6 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:43 p.m.