Triple

T24967364
Position Surface form Disambiguated ID Type / Status
Subject Battle of Famars E624785 entity
Predicate commander P1061 FINISHED
Object François Joseph Kinsky
François Joseph Kinsky was an 18th-century Austrian nobleman and general in the Habsburg army, noted for his leadership during the French Revolutionary Wars.
E1661012 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: François Joseph Kinsky | Statement: [Battle of Famars, commander, François Joseph Kinsky]
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: François Joseph Kinsky
Triple: [Battle of Famars, commander, François Joseph Kinsky]
Generated description
François Joseph Kinsky was an 18th-century Austrian nobleman and general in the Habsburg army, noted for his leadership during the French Revolutionary Wars.

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_69e2ff24512481908e9a72315b8d0354 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f444d9526c81908b5ee00d062d27f7 completed May 1, 2026, 6:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048a3533c8190b75553b97ed1b4a5 completed May 22, 2026, 12:14 p.m.
NEDg Description generation batch_6a10498ee91081909f400a590f3646a7 completed May 22, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a104a7bba188190b4d819ed6c618086 completed May 22, 2026, 12:22 p.m.
Created at: April 18, 2026, 6 a.m.