Triple
T7664516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Navy ribbons |
E173592
|
entity |
| Predicate | notWornWith |
P78638
|
FINISHED |
| Object | physical training 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: physical training uniforms | Statement: [Navy ribbons, notWornWith, physical training uniforms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notWornWith Context triple: [Navy ribbons, notWornWith, physical training uniforms]
-
A.
notWornBy
Indicates that a particular item or object is explicitly not being worn or used as clothing/accessory by a specified entity.
-
B.
typicallyWornWith
Indicates that one item of clothing or accessory is commonly or customarily worn together with another.
-
C.
alsoWornIn
Indicates that an item of clothing or accessory is additionally worn in another context, location, or time beyond the primary one mentioned.
-
D.
wornAs
Indicates that one entity is used or put on as clothing, an accessory, or a wearable item by another entity.
-
E.
wears
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7063dab1881909598b04999b8b690 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015f7430819099d3ea2781b7cee2 |
completed | March 27, 2026, 10:14 p.m. |
| PDg | Predicate description generation | batch_69c7063cfd78819095c6501fe8d57312 |
completed | March 27, 2026, 10:35 p.m. |
Created at: March 27, 2026, 4 p.m.