Triple
T16901921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | coat of arms of the British Virgin Islands |
E424457
|
entity |
| Predicate | figureCount |
P6685
|
FINISHED |
| Object | one female figure |
—
|
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: one female figure | Statement: [coat of arms of the British Virgin Islands, figureCount, one female figure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: figureCount Context triple: [coat of arms of the British Virgin Islands, figureCount, one female figure]
-
A.
numberOfFiguresDepicted
chosen
Indicates the total count of distinct figures shown within a given depiction or representation.
-
B.
figureType
Indicates that one entity is classified as a specific type or category of figure in relation to another entity.
-
C.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
-
D.
numberOfFlowersDepicted
Indicates the quantity of flowers shown or represented in an image or depiction.
-
E.
tileCount
Indicates the number of tiles associated with or contained by a given entity or area.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8dd2bf08190b1dc099e8a23cd04 |
completed | April 18, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69e32b9489408190bcb2ede567ff5bf9 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:29 a.m.