Triple
T2091420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine de’ Medici |
E32678
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Caterina |
E64628
|
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: Caterina | Statement: [Catherine de’ Medici, givenName, Caterina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caterina Context triple: [Catherine de’ Medici, givenName, Caterina]
-
A.
Caterina
chosen
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
B.
Caterina Tezio
Caterina Tezio was the wife of renowned Italian Baroque sculptor and architect Gian Lorenzo Bernini.
-
C.
Benedetta
Benedetta is an Italian feminine given name, equivalent to "Benedicta" and commonly used in Italy and other Italian-speaking communities.
-
D.
Carmelina
Carmelina is a lesser-known Broadway musical with music by Burton Lane and lyrics by Alan Jay Lerner, loosely based on the film "Buona Sera, Mrs. Campbell."
-
E.
Leonora
Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
- 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba7626d081908c9c0f18942e128d |
completed | March 7, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5189bc088190b142dde85cad6676 |
completed | March 9, 2026, 4:50 a.m. |
Created at: March 4, 2026, 7:43 p.m.