Triple

T37189929
Position Surface form Disambiguated ID Type / Status
Subject Odo, Count of Penthièvre E921421 entity
Predicate child P120 FINISHED
Object Etiennette of Brittany
Etiennette of Brittany was a medieval Breton noblewoman, known as the daughter of Odo, Count of Penthièvre, and a member of the ducal House of Brittany.
E2285878 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: Etiennette of Brittany | Statement: [Odo, Count of Penthièvre, child, Etiennette of Brittany]
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: Etiennette of Brittany
Triple: [Odo, Count of Penthièvre, child, Etiennette of Brittany]
Generated description
Etiennette of Brittany was a medieval Breton noblewoman, known as the daughter of Odo, Count of Penthièvre, and a member of the ducal House of Brittany.

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_69f76ea313a08190a54404cd1e47da90 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb361a9ce0819088c145f704f3f9fd completed May 6, 2026, 12:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a46295133648190aa2941eddcfed41e completed July 2, 2026, 9:03 a.m.
NEDg Description generation batch_6a462a3f18348190bc1eeb5af88330f4 completed July 2, 2026, 9:07 a.m.
NED2 Entity disambiguation (via description) batch_6a46300bca408190a9324196b6deeabd completed July 2, 2026, 9:31 a.m.
Created at: May 3, 2026, 4:15 p.m.