Triple
T164267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ginevra de’ Benci |
E2976
|
entity |
| Predicate | inscriptionTranslation |
P5528
|
FINISHED |
| Object | “Beauty adorns virtue” |
—
|
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: “Beauty adorns virtue” | Statement: [Ginevra de’ Benci, inscriptionTranslation, “Beauty adorns virtue”]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inscriptionTranslation Context triple: [Ginevra de’ Benci, inscriptionTranslation, “Beauty adorns virtue”]
-
A.
reverseInscription
Indicates that one entity is inscribed as the reverse or mirror image of another entity’s inscription.
-
B.
inscription
Indicates that text has been written, carved, or engraved onto a surface or object.
-
C.
bellInscriptionLanguage
Indicates the language in which the inscription on a bell is written.
-
D.
isInscribedOn
Indicates that text, symbols, or markings are written, carved, or otherwise permanently placed onto the surface of an object.
-
E.
inscribedOn
Indicates that text, symbols, or markings are written or carved onto the surface of an object.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258827da481909b20ea5e9d21676f |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a2566392208190a538ea9aa1fac53e |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a257101060819094db0f3a3a72f312 |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.