Triple

T34031977
Position Surface form Disambiguated ID Type / Status
Subject John I, Duke of Lorraine E872679 entity
Predicate givenName P17 FINISHED
Object John
John I, Duke of Lorraine, was a 14th-century French nobleman who ruled the Duchy of Lorraine from 1346 until his death in 1390.
E2078296 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: John | Statement: [John I, Duke of Lorraine, givenName, John]
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: John
Triple: [John I, Duke of Lorraine, givenName, John]
Generated description
John I, Duke of Lorraine, was a 14th-century French nobleman who ruled the Duchy of Lorraine from 1346 until his death in 1390.

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_69f349a2527c81909a7cd4bda94d70ad completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70b2197488190bd1c7bcee940b19a completed May 3, 2026, 8:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36a021e22c81909330b103247b77eb completed June 20, 2026, 2:13 p.m.
NEDg Description generation batch_6a36a0987aa48190a9dd1643b2d0a2ad completed June 20, 2026, 2:15 p.m.
NED2 Entity disambiguation (via description) batch_6a36a130088081909457ea8bf747083a completed June 20, 2026, 2:18 p.m.
Created at: May 1, 2026, 1:51 a.m.