Triple
T15420458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Order of Saint Januarius |
E369359
|
entity |
| Predicate | orderOfWear |
P118717
|
FINISHED |
| Object | highest Bourbon-Two Sicilies order |
—
|
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: highest Bourbon-Two Sicilies order | Statement: [Order of Saint Januarius, orderOfWear, highest Bourbon-Two Sicilies order]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orderOfWear Context triple: [Order of Saint Januarius, orderOfWear, highest Bourbon-Two Sicilies order]
-
A.
orderOfWearInUK
Indicates the sequence or priority in which items are worn in the UK context (e.g., clothing or insignia), relative to other items.
-
B.
wornFor
Indicates that an item is worn for a particular purpose, function, or occasion.
-
C.
orderOfWearWithinOrder
Indicates the sequence in which items are worn relative to each other within a given ordering or outfit configuration.
-
D.
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).
-
E.
wornAt
Indicates that an item is being worn on a specific part of the body or at a particular time or event.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ebe7b1081908e6b9e6e128a8d5d |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded27f45548190a6d2b1b85cb47444 |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57005608190886cd01f640dfedb |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 3:20 a.m.