Triple
T34880964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States military badges |
E1006007
|
entity |
| Predicate | areWornOn |
P181966
|
FINISHED |
| Object | military 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: military uniforms | Statement: [United States military badges, areWornOn, military uniforms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areWornOn Context triple: [United States military badges, areWornOn, military uniforms]
-
A.
wears
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
B.
wornAround
Indicates that one entity is physically worn encircling or surrounding another entity (e.g., around a body part or object).
-
C.
wearsOnBodyPart
Indicates that an entity places or has an item worn on a specific part of its body.
-
D.
wornOver
Indicates that one item of clothing or accessory is positioned on top of and covering another item when worn.
-
E.
wornAs
Indicates that one entity is used or put on as clothing, an accessory, or a wearable item by another entity.
- F. None of above. chosen
Provenance (4 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_69f76dbedb288190afe5780710847410 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782f4f10081908f97f6d0d2dbeec7 |
completed | May 3, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69f780ff71cc8190a67e71076fbad81a |
completed | May 3, 2026, 5:08 p.m. |
| PDg | Predicate description generation | batch_69f782f416c081908bdd9b1ad456f0e2 |
completed | May 3, 2026, 5:16 p.m. |
Created at: May 3, 2026, 4 p.m.