Triple

T28412927
Position Surface form Disambiguated ID Type / Status
Subject Manon E719717 entity
Predicate character P662 FINISHED
Object Guillot de Morfontaine
Guillot de Morfontaine is a wealthy, lecherous nobleman who serves as a comic yet morally dubious antagonist in Jules Massenet’s opera *Manon*.
 E1817236 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: Guillot de Morfontaine | Statement: [Manon, character, Guillot de Morfontaine]
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: Guillot de Morfontaine
Triple: [Manon, character, Guillot de Morfontaine]
Generated description
Guillot de Morfontaine is a wealthy, lecherous nobleman who serves as a comic yet morally dubious antagonist in Jules Massenet’s opera *Manon*.

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_69eff6f0f37c8190b37bc6fab08a9449 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64dbe401081909ea87d252a09101d completed May 2, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16331852fc81909baffca8b4f9ce62 completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a1634c284bc8190a09d836655486dc7 completed May 27, 2026, 12:03 a.m.
NED2 Entity disambiguation (via description) batch_6a1638ab3f5c8190be17ee9121039cd1 completed May 27, 2026, 12:19 a.m.
Created at: April 28, 2026, 1:28 a.m.