Triple
T1011989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Arms of the Kingdom of Scotland |
E21843
|
entity |
| Predicate | tressureTincture |
P23236
|
FINISHED |
| Object | gules |
—
|
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: gules | Statement: [Royal Arms of the Kingdom of Scotland, tressureTincture, gules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tressureTincture Context triple: [Royal Arms of the Kingdom of Scotland, tressureTincture, gules]
-
A.
treats
Indicates that one entity provides medical care or therapeutic intervention to another entity.
-
B.
sacredTruce
Indicates a formally recognized period during which hostilities or conflicts are suspended, often for religious or ceremonial reasons.
-
C.
typeOfRemedy
Indicates that one entity is a specific kind or category of remedy in relation to another entity.
-
D.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
E.
typeOfMagic
Indicates that one entity is a specific category, school, or kind of magic associated with 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_69a493c68e24819080ed0ee8bcfd5ce0 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7be907c8190b5c6ea89257755a7 |
completed | March 1, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69a4b72207c08190a3dbb2aa7acbbc71 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b7bd3d50819091e6f1d2ffe4c7ee |
completed | March 1, 2026, 10:03 p.m. |
Created at: March 1, 2026, 7:41 p.m.