Triple

T27307348
Position Surface form Disambiguated ID Type / Status
Subject Duke of Joyeuse E689099 entity
Predicate precededBy P97 FINISHED
Object Viscount of Joyeuse
The Viscount of Joyeuse was a French noble title held by members of the Joyeuse family before their elevation to higher ranks in the peerage.
 E1774252 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: Viscount of Joyeuse | Statement: [Duke of Joyeuse, precededBy, Viscount of Joyeuse]
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: Viscount of Joyeuse
Triple: [Duke of Joyeuse, precededBy, Viscount of Joyeuse]
Generated description
The Viscount of Joyeuse was a French noble title held by members of the Joyeuse family before their elevation to higher ranks in the peerage.

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_69ef355b931c8190a63cafaf7bcc008b completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f627b0ae548190bbdacafe61147088 completed May 2, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbc59d688190a8dabada829eb8f4 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bc771b0481909cca1c87c805f0de completed May 24, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd3f12b481908606b8e373ff408a completed May 24, 2026, 8:56 a.m.
Created at: April 27, 2026, 11:25 a.m.