Triple
T13201917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Largo di Santa Susanna |
E314261
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Santa Susanna |
E314256
|
NE 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: Santa Susanna | Statement: [Largo di Santa Susanna, namedAfter, Santa Susanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Susanna Context triple: [Largo di Santa Susanna, namedAfter, Santa Susanna]
-
A.
Saint Susanna
chosen
Saint Susanna is a Christian martyr venerated as a saint, traditionally associated with early Roman Christianity and honored in churches bearing her name.
-
B.
Susanna
Susanna is a feminine given name of Hebrew origin, commonly used in various European languages and cultures.
-
C.
Susanna
Susanna is a deuterocanonical addition to the Book of Daniel, telling the story of a virtuous woman falsely accused of adultery and vindicated by the prophet Daniel.
-
D.
Sant Anna
Sant Anna is a variant spelling of the Italian name Sant’Anna, commonly referring to places, institutions, or entities named after Saint Anne.
-
E.
Santa Ifigênia
Santa Ifigênia is a historic central neighborhood in São Paulo, Brazil, known for its bustling electronics commerce and proximity to major downtown landmarks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c6591d881909a6ebc22246caead |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716c2c15c819096a0eb84e7551f6f |
completed | May 3, 2026, 9:34 a.m. |
Created at: April 9, 2026, 9:16 p.m.