Triple

T25086589
Position Surface form Disambiguated ID Type / Status
Subject Louis III of Anjou E628339 entity
Predicate givenName P17 FINISHED
Object Louis
Louis was the given name of Louis III of Anjou, a late 14th- to early 15th-century French nobleman and claimant to the Kingdom of Naples.
E1660472 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 | Statement: [Louis III of Anjou, givenName, Louis]
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
Triple: [Louis III of Anjou, givenName, Louis]
Generated description
Louis was the given name of Louis III of Anjou, a late 14th- to early 15th-century French nobleman and claimant to the Kingdom of Naples.

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_69e2ff2f58e881908340527bc5d34f07 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f461e4d88c8190a81861b733d534ac completed May 1, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048c44c748190ab184ba085a0d92b completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a1049b7fa908190a51985413a9d3612 completed May 22, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_6a104a151da88190a2d8aba44f1dd924 completed May 22, 2026, 12:20 p.m.
Created at: April 18, 2026, 6:23 a.m.