Triple
T18941588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of San Marino |
E463394
|
entity |
| Predicate | featherCountPerTower |
P54090
|
FINISHED |
| Object | one ostrich feather |
—
|
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 ostrich feather | Statement: [Coat of arms of San Marino, featherCountPerTower, one ostrich feather]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featherCountPerTower Context triple: [Coat of arms of San Marino, featherCountPerTower, one ostrich feather]
-
A.
numberOfFeathers
chosen
Indicates the specific count of feathers associated with an entity.
-
B.
numberOfTowers
Indicates the quantity of towers associated with or contained by a given entity.
-
C.
featherCountSymbolizes
Indicates that the number of feathers associated with an entity represents or stands for some other property, status, or concept related to that entity.
-
D.
storeysOfTallestTower
Indicates the number of storeys contained in the tallest tower associated with the given context or entity.
-
E.
turretCount
Indicates the number of turrets associated with or mounted on a given 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d3eba60081909c9027d1988f88ad |
completed | April 20, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69e4a2efec5c8190840704016bf547a1 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 11:59 a.m.