Triple
T3212460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Fourragere |
E67310
|
entity |
| Predicate | regulatesWear |
P21364
|
FINISHED |
| Object | French military dress regulations |
—
|
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 military dress regulations | Statement: [French Fourragere, regulatesWear, French military dress regulations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regulatesWear Context triple: [French Fourragere, regulatesWear, French military dress regulations]
-
A.
wears
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
B.
wearAuthorizedFor
chosen
Indicates that an entity is permitted or authorized to wear a specified item (such as clothing, equipment, or insignia).
-
C.
regulatesWith
Indicates that one entity controls, modulates, or influences the activity, state, or behavior of another entity through some regulatory mechanism or interaction.
-
D.
wearingClass
Indicates that one entity is wearing or dressed in an item belonging to a particular class or category of clothing or accessories.
-
E.
regulatesUse
Indicates that one entity controls, governs, or sets rules for how another entity may be used.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaabba8e481909118d9f888ddcd63 |
completed | March 8, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69ad9e09b83881908801d79c3d9254f9 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:07 p.m.