Triple

T31608547
Position Surface form Disambiguated ID Type / Status
Subject Saint Louis Martin E806560 entity
Predicate child P120 FINISHED
Object Marie Hélène
Marie Hélène was one of the daughters of Saint Louis Martin, the French layman and father of Saint Thérèse of Lisieux.
 E1988082 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: Marie Hélène | Statement: [Saint Louis Martin, child, Marie Hélène]
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: Marie Hélène
Triple: [Saint Louis Martin, child, Marie Hélène]
Generated description
Marie Hélène was one of the daughters of Saint Louis Martin, the French layman and father of Saint Thérèse of Lisieux.

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_69f348d61f2081908cad94bc9ffbb671 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a87155008190a0e37892fce005eb completed May 3, 2026, 1:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ed4c8c3f48190933b5e0cc796d7ec completed June 14, 2026, 4:20 p.m.
NEDg Description generation batch_6a2ed55fcd2c8190a2168167792d253d completed June 14, 2026, 4:22 p.m.
NED2 Entity disambiguation (via description) batch_6a2ed6052b008190a0f55e32f516645c completed June 14, 2026, 4:25 p.m.
Created at: April 30, 2026, 10:35 p.m.