Triple

T37234333
Position Surface form Disambiguated ID Type / Status
Subject Carmen (Carmen Suite) E923526 entity
Predicate hasRelationshipWith P2830 FINISHED
Object Escamillo (Carmen Suite)
Escamillo (Carmen Suite) is the charismatic toreador whose bold, swaggering music and character provide a dramatic counterpoint to Carmen and Don José in the orchestral adaptation of Bizet’s opera.
E2221468 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: Escamillo (Carmen Suite) | Statement: [Carmen (Carmen Suite), hasRelationshipWith, Escamillo (Carmen Suite)]
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: Escamillo (Carmen Suite)
Triple: [Carmen (Carmen Suite), hasRelationshipWith, Escamillo (Carmen Suite)]
Generated description
Escamillo (Carmen Suite) is the charismatic toreador whose bold, swaggering music and character provide a dramatic counterpoint to Carmen and Don José in the orchestral adaptation of Bizet’s opera.

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_69f76ea9fee88190a589f661d95a7189 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb36ce25cc8190b56c4e5520bac0b9 completed May 6, 2026, 12:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4051220e7c8190a9e363d8f66587b9 completed June 27, 2026, 10:39 p.m.
NEDg Description generation batch_6a405d484fdc81909cb31d922860ead9 completed June 27, 2026, 11:31 p.m.
NED2 Entity disambiguation (via description) batch_6a405d9e1d108190847b74168cab9a5b completed June 27, 2026, 11:32 p.m.
Created at: May 3, 2026, 4:15 p.m.