Triple

T30865044
Position Surface form Disambiguated ID Type / Status
Subject Une vie E786174 entity
Predicate titleInEnglish P6688 FINISHED
Object A Woman’s Life
A Woman’s Life is a 2016 French period drama film, based on Guy de Maupassant’s novel, that follows the hardships and disillusionments of a young noblewoman in 19th-century Normandy.
 E1936216 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: A Woman’s Life | Statement: [Une vie, titleInEnglish, A Woman’s Life]
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: A Woman’s Life
Triple: [Une vie, titleInEnglish, A Woman’s Life]
Generated description
A Woman’s Life is a 2016 French period drama film, based on Guy de Maupassant’s novel, that follows the hardships and disillusionments of a young noblewoman in 19th-century Normandy.

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_69f224b9df2c819086f55f8bcf7f382e completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f691ac00448190b6b89a8c4cb0c9c0 completed May 3, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28c7d629f88190b41dc6bdfed32976 completed June 10, 2026, 2:11 a.m.
NEDg Description generation batch_6a28cc6167d481909f39e735e9ac5b77 completed June 10, 2026, 2:30 a.m.
NED2 Entity disambiguation (via description) batch_6a28cdb1ee7481909395b195f16c6163 completed June 10, 2026, 2:36 a.m.
Created at: April 29, 2026, 8:47 p.m.