Triple

T19795471
Position Surface form Disambiguated ID Type / Status
Subject Trial of Marie Antoinette E475528 entity
Predicate hasDefenseLawyer P3011 FINISHED
Object Jacques-Bernard-Marie Montané
Jacques-Bernard-Marie Montané was a French revolutionary-era lawyer and official who served in prominent legal roles during the French Revolution, including in high-profile political trials.
E2201694 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: Jacques-Bernard-Marie Montané | Statement: [Trial of Marie Antoinette, hasDefenseLawyer, Jacques-Bernard-Marie Montané]
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: Jacques-Bernard-Marie Montané
Triple: [Trial of Marie Antoinette, hasDefenseLawyer, Jacques-Bernard-Marie Montané]
Generated description
Jacques-Bernard-Marie Montané was a French revolutionary-era lawyer and official who served in prominent legal roles during the French Revolution, including in high-profile political trials.

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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653c659088190928fa4c9264135d3 completed April 20, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3dde3b26208190bb6200dedb79d2a4 completed June 26, 2026, 2:04 a.m.
NEDg Description generation batch_6a3ddeeb3e188190beb8c8e0b0cfff8a completed June 26, 2026, 2:07 a.m.
NED2 Entity disambiguation (via description) batch_6a3df4440fe881908f09bcfd56aea205 completed June 26, 2026, 3:38 a.m.
Created at: April 10, 2026, 1:49 p.m.