Triple

T5680420
Position Surface form Disambiguated ID Type / Status
Subject Columbine E125184 entity
Predicate hasVariant P455 FINISHED
Object Colombina
Colombina is a clever, flirtatious maid character from the Italian commedia dell’arte tradition, often portrayed as Harlequin’s witty and resourceful lover.
 E544767 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: Colombina | Statement: [Columbine, hasVariant, Colombina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Colombina
Context triple: [Columbine, hasVariant, Colombina]
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Paola
    Paola is a town in southeastern Malta known for its historic sites, including the prehistoric Ħal Saflieni Hypogeum and other cultural landmarks.
  • C. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • 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. Letizia
    Letizia is a feminine given name of Italian origin, famously borne by Maria Letizia Ramolino, the mother of Napoleon Bonaparte.
  • 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: Colombina
Triple: [Columbine, hasVariant, Colombina]
Generated description
Colombina is a clever, flirtatious maid character from the Italian commedia dell’arte tradition, often portrayed as Harlequin’s witty and resourceful lover.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Colombina
Target entity description: Colombina is a clever, flirtatious maid character from the Italian commedia dell’arte tradition, often portrayed as Harlequin’s witty and resourceful lover.
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • C. Paola
    Paola is a town in southeastern Malta known for its historic sites, including the prehistoric Ħal Saflieni Hypogeum and other cultural landmarks.
  • 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. Letizia
    Letizia is a feminine given name of Italian origin, famously borne by Maria Letizia Ramolino, the mother of Napoleon Bonaparte.
  • 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_69c0082a884c8190a79001bae658941f completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02397c01081909793bb53ad7cbbce completed March 22, 2026, 5:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dcb28888190affb0982060d5ea8 completed March 22, 2026, 11:39 p.m.
NEDg Description generation batch_69c08d7c1040819099f2f2fafdbc9627 completed March 23, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_69c08ddef5548190826d723551e99133 completed March 23, 2026, 12:48 a.m.
Created at: March 22, 2026, 3:44 p.m.