Triple
T4022913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australian Multicam Camouflage Uniform |
E91319
|
entity |
| Predicate | hasCamouflageType |
P1200
|
FINISHED |
| Object | multicam |
—
|
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: multicam | Statement: [Australian Multicam Camouflage Uniform, hasCamouflageType, multicam]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCamouflageType Context triple: [Australian Multicam Camouflage Uniform, hasCamouflageType, multicam]
-
A.
camouflagePattern
chosen
Indicates that one entity has a surface or visual design intended to conceal it by blending with its surroundings or disrupting its outline.
-
B.
camouflageEffectiveness
Indicates how well one entity’s appearance or behavior conceals it from detection by another entity or sensing system.
-
C.
hasTypeOfInsignia
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
D.
usesMasksOrDisguises
Indicates that an entity employs masks, costumes, or other forms of disguise to conceal or alter its identity in the context of an action or interaction.
-
E.
hasCape
Indicates that one entity possesses or is wearing a cape.
- 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_69aed9618b04819081750d979d2af098 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaccb2f48190a16a1ba6e938da85 |
completed | March 9, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69aef8fc78ec819092d4dab88d85a141 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:35 p.m.