Triple
T2535718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batavian army |
E56263
|
entity |
| Predicate | usedUniformStyle |
P30520
|
FINISHED |
| Object | French-inspired uniforms |
—
|
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: French-inspired uniforms | Statement: [Batavian army, usedUniformStyle, French-inspired uniforms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedUniformStyle Context triple: [Batavian army, usedUniformStyle, French-inspired uniforms]
-
A.
usesUniform
Indicates that one entity regularly wears or employs a standardized set of clothing or equipment designated as a uniform.
-
B.
usedWithStyle
Indicates that something is employed or applied in conjunction with a particular style or stylistic manner.
-
C.
usesAsStyleOf
chosen
Indicates that one entity adopts or applies another entity as a stylistic model, method, or manner of expression.
-
D.
usedUniformlyAcrossCountry
Indicates that something is applied or practiced in the same way throughout the entire country without regional variation.
-
E.
styleTendsTo
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
- 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd64a2194819097c66cbeb37fe859 |
completed | March 7, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69abd0c4a5dc819097812db50443420a |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:47 p.m.