Triple

T24485089
Position Surface form Disambiguated ID Type / Status
Subject Count of Forcalquier E617482 entity
Predicate titleHolder P1911 FINISHED
Object Guigues VI of Viennois
Guigues VI of Viennois was a 13th-century French nobleman from the Dauphiné region who held multiple important feudal titles and played a role in the complex politics of southeastern France.
E1650761 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: Guigues VI of Viennois | Statement: [Count of Forcalquier, titleHolder, Guigues VI of Viennois]
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: Guigues VI of Viennois
Triple: [Count of Forcalquier, titleHolder, Guigues VI of Viennois]
Generated description
Guigues VI of Viennois was a 13th-century French nobleman from the Dauphiné region who held multiple important feudal titles and played a role in the complex politics of southeastern France.

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_69e2d7f3ae788190b683394db15f220e completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f2a6daa6008190aaac3e5330f842cd completed April 30, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101bdc4dc4819081d422be8c0fc3be completed May 22, 2026, 9:03 a.m.
NEDg Description generation batch_6a10248751648190aabfa72ad8ab0b3f completed May 22, 2026, 9:40 a.m.
NED2 Entity disambiguation (via description) batch_6a102586c1288190bf8eeb513537b189 completed May 22, 2026, 9:44 a.m.
Created at: April 18, 2026, 2:21 a.m.