Triple
T27134788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Buses |
E681652
|
entity |
| Predicate | usedVehicleMarking |
P104330
|
FINISHED |
| Object | Red Cross emblem |
—
|
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: Red Cross emblem | Statement: [White Buses, usedVehicleMarking, Red Cross emblem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedVehicleMarking Context triple: [White Buses, usedVehicleMarking, Red Cross emblem]
-
A.
usesMarkedVehicles
chosen
Indicates that an entity carries out its activities or operations using vehicles that are visibly marked or identified for that purpose.
-
B.
usedUnmarkedVehicles
Indicates that the action or operation was carried out using vehicles that bore no identifying marks, logos, or official insignia.
-
C.
armorMarkings
Indicates that one entity bears specific markings, patterns, or insignia on its armor in relation to another entity or context.
-
D.
coatMarkings
Indicates how an entity’s coat is patterned or marked, such as stripes, spots, or other distinctive visual markings.
-
E.
usedVehicleModel
Indicates that a vehicle is a pre-owned (used) instance of a particular vehicle model.
- 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_69eefacbcc2081909ebf00daa23f1981 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f62479bbb88190bcad383443cbd638 |
completed | May 2, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69f620e38aec8190bb184edcdbd6da64 |
completed | May 2, 2026, 4:05 p.m. |
Created at: April 27, 2026, 9:06 a.m.