Triple
T17302171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juno Sospita |
E420064
|
entity |
| Predicate | hasEpithet |
P23283
|
FINISHED |
| Object |
Sospita
Sospita is an epithet of the Roman goddess Juno emphasizing her role as a protector and savior, especially of women and the state.
|
E1261211
|
NE FINISHED |
How this triple was built (4 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: Sospita | Statement: [Juno Sospita, hasEpithet, Sospita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sospita Context triple: [Juno Sospita, hasEpithet, Sospita]
-
A.
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."
-
B.
Caterina
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
C.
Benedetta
Benedetta is an Italian feminine given name, equivalent to "Benedicta" and commonly used in Italy and other Italian-speaking communities.
-
D.
Agnese
Agnese is an Italian given name, equivalent to the English name Agnes, traditionally associated with Christian saints and classical European usage.
-
E.
Donata
Donata is an Italian feminine given name of Latin origin, traditionally meaning "given" or "gifted."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sospita Triple: [Juno Sospita, hasEpithet, Sospita]
Generated description
Sospita is an epithet of the Roman goddess Juno emphasizing her role as a protector and savior, especially of women and the state.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sospita Target entity description: Sospita is an epithet of the Roman goddess Juno emphasizing her role as a protector and savior, especially of women and the state.
-
A.
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."
-
B.
Caterina
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
C.
Benedetta
Benedetta is an Italian feminine given name, equivalent to "Benedicta" and commonly used in Italy and other Italian-speaking communities.
-
D.
Agnese
Agnese is an Italian given name, equivalent to the English name Agnes, traditionally associated with Christian saints and classical European usage.
-
E.
Donata
Donata is an Italian feminine given name of Latin origin, traditionally meaning "given" or "gifted."
- F. None of above. chosen
Provenance (5 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_69d886db32608190a61e18862c5a8af6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e438fba938819084333764b868bd83 |
completed | April 19, 2026, 2:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180dc929c819096a7a5dc81e5b6ef |
completed | May 11, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_6a0181ae2d588190a4ff68094529a994 |
completed | May 11, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0182ccd104819088569cf0be87b4d3 |
completed | May 11, 2026, 7:18 a.m. |
Created at: April 10, 2026, 5:41 a.m.