Triple
T6276928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | coat of arms of Sierra Leone |
E140682
|
entity |
| Predicate | tinctureWaves |
P69812
|
FINISHED |
| Object | azure and argent |
—
|
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: azure and argent | Statement: [coat of arms of Sierra Leone, tinctureWaves, azure and argent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tinctureWaves Context triple: [coat of arms of Sierra Leone, tinctureWaves, azure and argent]
-
A.
tinctureOfField
Indicates that one entity is a tincture (medicinal extract or solution) derived from, based on, or primarily composed of another entity representing a field or source material.
-
B.
tressureTincture
Indicates the color or pattern (tincture) applied specifically to a tressure in heraldic design.
-
C.
tinctureOrdinary
Indicates that an ordinary (a basic heraldic shape or charge) is depicted with a specific tincture (color, metal, or fur) in a coat of arms.
-
D.
tinctureOfLion
Indicates a relationship where something is a medicinal or alchemical preparation (a “tincture”) derived from or associated with a lion.
-
E.
tongueTincture
Indicates that an entity applies or administers a tincture by placing it on or under the tongue.
- 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_69c008cc158881908df6ec94a911c736 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063d96fbc8190a9091456b82762d1 |
completed | March 22, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69c05608a5608190b22a1fdc4060470d |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c05b37ac1881909e947822490ba4f2 |
completed | March 22, 2026, 9:12 p.m. |
Created at: March 22, 2026, 4:26 p.m.