Triple
T26990644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sea Service Deployment Ribbon |
E679851
|
entity |
| Predicate | orderOfWearRelativeTo |
P118717
|
FINISHED |
| Object | Navy Arctic Service Ribbon |
—
|
NE NERFINISHED |
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: Navy Arctic Service Ribbon | Statement: [Sea Service Deployment Ribbon, orderOfWearRelativeTo, Navy Arctic Service Ribbon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orderOfWearRelativeTo Context triple: [Sea Service Deployment Ribbon, orderOfWearRelativeTo, Navy Arctic Service Ribbon]
-
A.
orderOfWear
chosen
Indicates the relative sequence in which items are intended to be worn, specifying which item is put on before or after another.
-
B.
orderOfWearWithinOrder
Indicates the sequence in which items are worn relative to each other within a given ordering or outfit configuration.
-
C.
wearingOrder
Indicates the relative sequence in which items are worn on or over one another (e.g., which garment is worn over or under another).
-
D.
orderOfWearInUK
Indicates the sequence or priority in which items are worn in the UK context (e.g., clothing or insignia), relative to other items.
-
E.
orderOf
Indicates that one entity is arranged, ranked, or sequenced before or after another according to a specified ordering criterion.
- 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_69eeeb5138ac8190b3c273ddc659a54f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f67257b0448190a13011af81c81449 |
completed | May 2, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 27, 2026, 6:51 a.m.