Triple

T30558318
Position Surface form Disambiguated ID Type / Status
Subject de Montmorency E777762 entity
Predicate hasNotableMember P304 FINISHED
Object Louis de Montmorency
Louis de Montmorency was a French nobleman from the influential Montmorency family, prominent in the political and military affairs of France during the late Middle Ages and Renaissance.
E1957216 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: Louis de Montmorency | Statement: [de Montmorency, hasNotableMember, Louis de Montmorency]
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: Louis de Montmorency
Triple: [de Montmorency, hasNotableMember, Louis de Montmorency]
Generated description
Louis de Montmorency was a French nobleman from the influential Montmorency family, prominent in the political and military affairs of France during the late Middle Ages and Renaissance.

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_69f2249ed41c8190b175170ecfd6e1c5 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f688d72a60819094fe4a3b8bbb5ac4 completed May 2, 2026, 11:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2a1e0972d48190813be5609ab3c272 completed June 11, 2026, 2:31 a.m.
NEDg Description generation batch_6a2a2823d8408190b62a5e80e6878daf completed June 11, 2026, 3:14 a.m.
NED2 Entity disambiguation (via description) batch_6a2a288b41bc8190bfdc652f18191347 completed June 11, 2026, 3:16 a.m.
Created at: April 29, 2026, 8:21 p.m.