Triple
T17815467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Commander of the Order of Orange-Nassau |
E444825
|
entity |
| Predicate | ribbonWorn |
P11705
|
FINISHED |
| Object | around the neck |
—
|
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: around the neck | Statement: [Commander of the Order of Orange-Nassau, ribbonWorn, around the neck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ribbonWorn Context triple: [Commander of the Order of Orange-Nassau, ribbonWorn, around the neck]
-
A.
ribbonType
Indicates the specific kind or category of ribbon associated with an entity.
-
B.
wornAround
chosen
Indicates that one entity is physically worn encircling or surrounding another entity (e.g., around a body part or object).
-
C.
ribbonHasStripe
Indicates that a ribbon features one or more stripes as part of its pattern or design.
-
D.
ribbonName
Indicates the specific name or label assigned to a ribbon within a system or context.
-
E.
wearsRibbonOf
Indicates that one entity is adorned with or has attached to it a ribbon associated with another entity.
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4887e80688190a2ac3308b0e815c6 |
completed | April 19, 2026, 7:47 a.m. |
| PD | Predicate disambiguation | batch_69e3d8de28688190844b65acf6af54e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:14 a.m.