Triple
T6878180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vatican City State Philatelic and Numismatic Office |
E158722
|
entity |
| Predicate | subjectAreaOfIssues |
P450
|
FINISHED |
| Object | religious themes |
—
|
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: religious themes | Statement: [Vatican City State Philatelic and Numismatic Office, subjectAreaOfIssues, religious themes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectAreaOfIssues Context triple: [Vatican City State Philatelic and Numismatic Office, subjectAreaOfIssues, religious themes]
-
A.
keyIssueArea
Indicates that something is a primary topic, domain, or field that is central or especially important within a broader context or discussion.
-
B.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
C.
subjectAreaLevel
Indicates the hierarchical level or depth of specialization of a particular subject area in relation to others.
-
D.
thematicArea
Indicates the subject or item is associated with, or falls under, a particular thematic area or topic of focus.
-
E.
involvesIssue
Indicates that an action, event, or entity is related to, concerns, or includes a particular issue.
- 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_69c68832af1481908ce356e133ebaebe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8e498bc81908b2fbe0c6a8b95b7 |
completed | March 27, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b363dc8190a7225b540ab2bc40 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:22 p.m.